This document summarizes a research paper that proposes using a technique called "tiny video representation" to classify and retrieve video frames and videos. The proposed method involves preprocessing videos by splitting them into frames, removing black bars, resizing frames to 32x32 pixels, and using affinity propagation to cluster unique frames. This creates a "tiny video database" that can be used for content-based copy detection, video categorization through classification of frames, and retrieval of related videos through nearest neighbor searches. Experimental results showed the tiny video database approach improved classification precision and recall compared to using individual frames or videos.
Conference research paper_target_trackingpatrobadri
The document proposes a 3-stage algorithm for real-time video object tracking on the DaVinci processor:
1. A novel object segmentation and background subtraction algorithm is designed to handle noise, illumination changes, and multiple moving objects.
2. Binary Large OBject (BLOB) detection is used to identify image clusters and solve problems of abrupt object shapes, sizes, and counts.
3. A centroid-based tracking method is used to improve robustness to occlusion and contour sliding.
Optimizations are applied at both the algorithm and code levels to reduce memory usage and access and improve execution speed, allowing the tracking of 30 frames per second in real-time. The algorithm provides at least a 2
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
Key frame extraction for video summarization using motion activity descriptorseSAT Journals
This document presents a method for video summarization using motion activity descriptors. It extracts key frames from videos by comparing motion between consecutive frames using block matching algorithms like diamond search and three step search. These algorithms determine which blocks to compare from consecutive frames to find the closest block match and derive a motion activity descriptor. Frames with high motion descriptors, indicating more difference between frames, are selected as key frames for the video summary. The method was tested on various video categories and showed high precision and summarization for some videos but lower values for others, depending on factors like scene changes, motion detectability, and object/area properties. An effective summary balances high precision with a high summarization factor by selecting frames that best represent the video's
Key frame extraction for video summarization using motion activity descriptorseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Passive techniques for detection of tampering in images by Surbhi Arora and S...arorasurbhi
This document summarizes research on passive techniques for detecting tampering in digital images. It discusses common types of tampering like copy-paste and describes approaches using rule-based and training-based methods. For rule-based, it evaluates exact match, robust match, and SURF features techniques. For training-based, it trains SVMs on block intensities, DWT/DFT moments, and SURF features. Testing showed the combination of Hu moments and block intensity had highest accuracy. While rule-based is not dependent on training data, training-based can detect more transformations but depends on training data quality and quantity. Future work involves improving rule-based for noise and SURF segmentation and adding more training images
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Conference research paper_target_trackingpatrobadri
The document proposes a 3-stage algorithm for real-time video object tracking on the DaVinci processor:
1. A novel object segmentation and background subtraction algorithm is designed to handle noise, illumination changes, and multiple moving objects.
2. Binary Large OBject (BLOB) detection is used to identify image clusters and solve problems of abrupt object shapes, sizes, and counts.
3. A centroid-based tracking method is used to improve robustness to occlusion and contour sliding.
Optimizations are applied at both the algorithm and code levels to reduce memory usage and access and improve execution speed, allowing the tracking of 30 frames per second in real-time. The algorithm provides at least a 2
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
Key frame extraction for video summarization using motion activity descriptorseSAT Journals
This document presents a method for video summarization using motion activity descriptors. It extracts key frames from videos by comparing motion between consecutive frames using block matching algorithms like diamond search and three step search. These algorithms determine which blocks to compare from consecutive frames to find the closest block match and derive a motion activity descriptor. Frames with high motion descriptors, indicating more difference between frames, are selected as key frames for the video summary. The method was tested on various video categories and showed high precision and summarization for some videos but lower values for others, depending on factors like scene changes, motion detectability, and object/area properties. An effective summary balances high precision with a high summarization factor by selecting frames that best represent the video's
Key frame extraction for video summarization using motion activity descriptorseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Passive techniques for detection of tampering in images by Surbhi Arora and S...arorasurbhi
This document summarizes research on passive techniques for detecting tampering in digital images. It discusses common types of tampering like copy-paste and describes approaches using rule-based and training-based methods. For rule-based, it evaluates exact match, robust match, and SURF features techniques. For training-based, it trains SVMs on block intensities, DWT/DFT moments, and SURF features. Testing showed the combination of Hu moments and block intensity had highest accuracy. While rule-based is not dependent on training data, training-based can detect more transformations but depends on training data quality and quantity. Future work involves improving rule-based for noise and SURF segmentation and adding more training images
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
This document describes a new technique called "encrypted sensing" for capturing fingerprint images using digital holography and double random phase encoding (DRPE) for encryption. The fingerprint image is optically encrypted during capture using two random phase masks. This reduces the risk of theft or leakage of personal biometric data. The encrypted hologram can be decrypted into a clear fingerprint image only when the correct decryption key is applied. Experimental results show the decrypted images can be accurately verified for authentication purposes.
TEXT DETECTION AND EXTRACTION FROM VIDEOS USING ANN BASED NETWORKijscai
With fast intensification of existing multimedia documents and mounting demand for information indexing and retrieval, much endeavor has been done on extracting the text from images and videos. The prime intention of the projected system is to spot and haul out the scene text from video. Extracting the scene text from video is demanding due to complex background, varying font size, different style, lower resolution and blurring, position, viewing angle and so on. In this paper we put forward a hybrid method where the two most well-liked text extraction techniques i.e. region based method and connected component (CC) based method comes together. Initially the video is split into frames and key frames obtained. Text region indicator (TRI) is being developed to compute the text prevailing confidence and
candidate region by performing binarization. Artificial Neural network (ANN) is used as the classifier and Optical Character Recognition (OCR) is used for character verification. Text is grouped by constructing the minimum spanning tree with the use of bounding box distance.
This document discusses techniques for effective compression of digital video. It introduces several key algorithms used in video compression, including discrete cosine transform (DCT) for spatial redundancy reduction, motion estimation (ME) for temporal redundancy reduction, and embedded zerotree wavelet (EZW) transforms. DCT is used to compress individual video frames by removing spatial correlations within frames. Motion estimation compares blocks of pixels between frames to find and encode motion vectors rather than full pixel values, reducing file size. Combined, these techniques can achieve high compression ratios while maintaining high video quality for storage and transmission.
5 ijaems sept-2015-9-video feature extraction based on modified lle using ada...INFOGAIN PUBLICATION
Locally linear embedding (LLE) is an unsupervised learning algorithm which computes the low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover non-linear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. In this paper, video feature extraction is done using modified LLE alongwith adaptive nearest neighbor approach to find the nearest neighbor and the connected components. The proposed feature extraction method is applied to a video. The video feature description gives a new tool for analysis of video.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
This document summarizes research on developing a low bit rate encoding system for video compression using vector quantization. It first discusses how vector quantization can achieve high compression ratios and has been used widely in image and speech coding. It then describes the methodology used, which involves taking video frames as input, downsampling the frames to extract pixels, applying vector quantization, and detecting edges on the compressed frames to check compression quality. Finally, it discusses the results of testing the approach on MATLAB and presents conclusions on the advantages of the proposed algorithm for very low bit rate video coding applications.
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
IRJET- Comparison and Simulation based Analysis of an Optimized Block Mat...IRJET Journal
This document compares an optimized block matching algorithm to the four step search algorithm. It first provides background on block matching algorithms and motion estimation techniques used in video compression. It then describes the existing four step search algorithm and its process of checking 17-27 points to find the best motion vector match. The document proposes a new simpler and more efficient four step search algorithm that separates the search area into quadrants. It checks 3 points in the first phase to select a quadrant, then finds the lowest cost point in the second phase to set as the new origin, reducing computational complexity compared to the standard four step search.
This document presents a novel approach for jointly optimizing spatial prediction and transform coding in video compression. It aims to improve performance and reduce complexity compared to existing techniques. The proposed method uses singular value decomposition (SVD) to compress images. SVD decomposes an image matrix into three matrices, allowing the image to be approximated using only a few singular values. This achieves compression by removing redundant information. The document outlines the SVD approach for image compression and measures compression performance using compression ratio and mean squared error between the original and compressed images. It then discusses trends in image and video coding, including combining natural and synthetic content. Finally, it provides a block diagram of the proposed system and compares its compression performance to existing discrete cosine transform-
Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based o...IJECEIAES
Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segment.
This document discusses a structural similarity based approach for efficient multi-view video coding. It begins with an introduction to multi-view video coding and the structural similarity index metric. It then proposes using structural similarity to exploit structural information between different video views. The method uses structural similarity for rate distortion optimization in encoding. Experimental results show the left and right views of a video, their structural similarity image, the decoded 3D video, and the achieved minimum distortion level. The document aims to improve multi-view video quality by using structural similarity during the encoding process.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
Inpainting scheme for text in video a surveyeSAT Journals
This document summarizes text detection and removal schemes for video sequences. It discusses two main phases - text detection and video inpainting. For text detection, it describes various visual feature extraction techniques like edge detection and texture analysis. It also discusses machine learning approaches like multilayer perceptrons and support vector machines. For inpainting, it discusses using wavelet transforms to approximate boundary data and fill in missing regions after text is removed. The goal is to restore occluded parts of video frames while maintaining spatial and temporal consistency.
This document discusses a hand gesture recognition system for underprivileged individuals. It begins by outlining the key steps in hand gesture recognition systems: image capture, pre-processing, segmentation, feature extraction and gesture recognition. It then goes into more detail on specific techniques for each step, such as thresholding and edge detection for segmentation. The document also covers applications like access control, sign language translation and future areas like biometric authentication. In conclusion, it proposes that hand gesture recognition can help disabled individuals communicate through accessible human-computer interaction.
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
Key frame extraction methodology for video annotationIAEME Publication
This document summarizes a research paper that proposes a key frame extraction methodology to facilitate video annotation. The methodology uses edge difference between consecutive video frames to determine if the content has significantly changed. Frames where the edge difference exceeds a threshold are selected as key frames. The algorithm calculates edge differences for all frame pairs in a video. It then computes statistics like mean and standard deviation to determine a threshold. Frames with differences above this threshold are extracted as key frames. The key frames extracted represent important content changes in the video. Extracting key frames reduces processing requirements for video annotation compared to analyzing all frames. The methodology was tested on videos from domains like transportation and performed well at selecting representative frames.
The document describes a proposed method for extracting captions from videos. It involves three main steps: 1) Caption detection uses a stroke filter to identify stroke-like edges in captions, filtering out edges from complex backgrounds. 2) Caption localization spatially localizes captions in each video frame using an SVM classifier and temporally localizes captions appearing across multiple frames. 3) Caption segmentation separates caption pixels from background pixels. The proposed method aims to improve efficiency and accuracy over previous methods by considering temporal features to avoid extracting the same caption repeatedly.
A VIDEO COMPRESSION TECHNIQUE UTILIZING SPATIO-TEMPORAL LOWER COEFFICIENTSIAEME Publication
With the advancement of communication in recent trends, video compression plays an important role in the transmission of information on social networking and for storage with limited memory capacity. Also the inadequate bandwidth for transmission and lower quality make video compression a serious phenomenon to consider in the field of communication. There is a need to improve the video compression process which can encode the video data with low computational complexity with better quality along with maintaining speed. In this work, a new technique is developed based on the block processing utilizing the lower coefficients between frames.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
This document describes a new technique called "encrypted sensing" for capturing fingerprint images using digital holography and double random phase encoding (DRPE) for encryption. The fingerprint image is optically encrypted during capture using two random phase masks. This reduces the risk of theft or leakage of personal biometric data. The encrypted hologram can be decrypted into a clear fingerprint image only when the correct decryption key is applied. Experimental results show the decrypted images can be accurately verified for authentication purposes.
TEXT DETECTION AND EXTRACTION FROM VIDEOS USING ANN BASED NETWORKijscai
With fast intensification of existing multimedia documents and mounting demand for information indexing and retrieval, much endeavor has been done on extracting the text from images and videos. The prime intention of the projected system is to spot and haul out the scene text from video. Extracting the scene text from video is demanding due to complex background, varying font size, different style, lower resolution and blurring, position, viewing angle and so on. In this paper we put forward a hybrid method where the two most well-liked text extraction techniques i.e. region based method and connected component (CC) based method comes together. Initially the video is split into frames and key frames obtained. Text region indicator (TRI) is being developed to compute the text prevailing confidence and
candidate region by performing binarization. Artificial Neural network (ANN) is used as the classifier and Optical Character Recognition (OCR) is used for character verification. Text is grouped by constructing the minimum spanning tree with the use of bounding box distance.
This document discusses techniques for effective compression of digital video. It introduces several key algorithms used in video compression, including discrete cosine transform (DCT) for spatial redundancy reduction, motion estimation (ME) for temporal redundancy reduction, and embedded zerotree wavelet (EZW) transforms. DCT is used to compress individual video frames by removing spatial correlations within frames. Motion estimation compares blocks of pixels between frames to find and encode motion vectors rather than full pixel values, reducing file size. Combined, these techniques can achieve high compression ratios while maintaining high video quality for storage and transmission.
5 ijaems sept-2015-9-video feature extraction based on modified lle using ada...INFOGAIN PUBLICATION
Locally linear embedding (LLE) is an unsupervised learning algorithm which computes the low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover non-linear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. In this paper, video feature extraction is done using modified LLE alongwith adaptive nearest neighbor approach to find the nearest neighbor and the connected components. The proposed feature extraction method is applied to a video. The video feature description gives a new tool for analysis of video.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
This document summarizes research on developing a low bit rate encoding system for video compression using vector quantization. It first discusses how vector quantization can achieve high compression ratios and has been used widely in image and speech coding. It then describes the methodology used, which involves taking video frames as input, downsampling the frames to extract pixels, applying vector quantization, and detecting edges on the compressed frames to check compression quality. Finally, it discusses the results of testing the approach on MATLAB and presents conclusions on the advantages of the proposed algorithm for very low bit rate video coding applications.
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
IRJET- Comparison and Simulation based Analysis of an Optimized Block Mat...IRJET Journal
This document compares an optimized block matching algorithm to the four step search algorithm. It first provides background on block matching algorithms and motion estimation techniques used in video compression. It then describes the existing four step search algorithm and its process of checking 17-27 points to find the best motion vector match. The document proposes a new simpler and more efficient four step search algorithm that separates the search area into quadrants. It checks 3 points in the first phase to select a quadrant, then finds the lowest cost point in the second phase to set as the new origin, reducing computational complexity compared to the standard four step search.
This document presents a novel approach for jointly optimizing spatial prediction and transform coding in video compression. It aims to improve performance and reduce complexity compared to existing techniques. The proposed method uses singular value decomposition (SVD) to compress images. SVD decomposes an image matrix into three matrices, allowing the image to be approximated using only a few singular values. This achieves compression by removing redundant information. The document outlines the SVD approach for image compression and measures compression performance using compression ratio and mean squared error between the original and compressed images. It then discusses trends in image and video coding, including combining natural and synthetic content. Finally, it provides a block diagram of the proposed system and compares its compression performance to existing discrete cosine transform-
Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based o...IJECEIAES
Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segment.
This document discusses a structural similarity based approach for efficient multi-view video coding. It begins with an introduction to multi-view video coding and the structural similarity index metric. It then proposes using structural similarity to exploit structural information between different video views. The method uses structural similarity for rate distortion optimization in encoding. Experimental results show the left and right views of a video, their structural similarity image, the decoded 3D video, and the achieved minimum distortion level. The document aims to improve multi-view video quality by using structural similarity during the encoding process.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
Inpainting scheme for text in video a surveyeSAT Journals
This document summarizes text detection and removal schemes for video sequences. It discusses two main phases - text detection and video inpainting. For text detection, it describes various visual feature extraction techniques like edge detection and texture analysis. It also discusses machine learning approaches like multilayer perceptrons and support vector machines. For inpainting, it discusses using wavelet transforms to approximate boundary data and fill in missing regions after text is removed. The goal is to restore occluded parts of video frames while maintaining spatial and temporal consistency.
This document discusses a hand gesture recognition system for underprivileged individuals. It begins by outlining the key steps in hand gesture recognition systems: image capture, pre-processing, segmentation, feature extraction and gesture recognition. It then goes into more detail on specific techniques for each step, such as thresholding and edge detection for segmentation. The document also covers applications like access control, sign language translation and future areas like biometric authentication. In conclusion, it proposes that hand gesture recognition can help disabled individuals communicate through accessible human-computer interaction.
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
Key frame extraction methodology for video annotationIAEME Publication
This document summarizes a research paper that proposes a key frame extraction methodology to facilitate video annotation. The methodology uses edge difference between consecutive video frames to determine if the content has significantly changed. Frames where the edge difference exceeds a threshold are selected as key frames. The algorithm calculates edge differences for all frame pairs in a video. It then computes statistics like mean and standard deviation to determine a threshold. Frames with differences above this threshold are extracted as key frames. The key frames extracted represent important content changes in the video. Extracting key frames reduces processing requirements for video annotation compared to analyzing all frames. The methodology was tested on videos from domains like transportation and performed well at selecting representative frames.
The document describes a proposed method for extracting captions from videos. It involves three main steps: 1) Caption detection uses a stroke filter to identify stroke-like edges in captions, filtering out edges from complex backgrounds. 2) Caption localization spatially localizes captions in each video frame using an SVM classifier and temporally localizes captions appearing across multiple frames. 3) Caption segmentation separates caption pixels from background pixels. The proposed method aims to improve efficiency and accuracy over previous methods by considering temporal features to avoid extracting the same caption repeatedly.
A VIDEO COMPRESSION TECHNIQUE UTILIZING SPATIO-TEMPORAL LOWER COEFFICIENTSIAEME Publication
With the advancement of communication in recent trends, video compression plays an important role in the transmission of information on social networking and for storage with limited memory capacity. Also the inadequate bandwidth for transmission and lower quality make video compression a serious phenomenon to consider in the field of communication. There is a need to improve the video compression process which can encode the video data with low computational complexity with better quality along with maintaining speed. In this work, a new technique is developed based on the block processing utilizing the lower coefficients between frames.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document describes the design, implementation, and simulation of a 2-GHz low noise amplifier (LNA). The LNA is designed using both lumped elements and distributed elements approaches. Key steps in the design process are discussed, including the use of the MESFET transistor, input and output matching networks, and performance analysis using the Smith Chart. The LNA provides a noise figure of 0.358 dB, gain of 16.778 dB, and meets other specifications. Simulation results show that the lumped elements approach achieves better performance than the distributed elements approach. The document outlines the design process and evaluation of LNAs to meet requirements for wireless communication systems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document summarizes a study characterizing the anodized film developed on titanium plates in a KOH bath. Key findings:
1. Anodizing titanium in a KOH bath between 20-72V produced films with colors ranging from blue to yellow to purple to green.
2. Analysis found the film consisted mainly of TiO2 and Ti2O3 and was uniform and compact.
3. Corrosion testing showed the film anodized at 50-52V exhibited the best corrosion resistance in salt spray, acid, and impedance tests, while films at lower and higher voltages had decreasing resistance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses the design and implementation of a network device driver in Linux using NAPI (New API) to improve performance. It begins with an introduction to network device drivers and challenges with high interrupt loads. It then describes NAPI and how it uses polling instead of interrupts to process packets. The rest of the document provides details on the specific NAPI implementation for an ARM920T processor, including advantages like reduced interrupt processing and packet dropping. It evaluates the performance improvement from using NAPI during high packet loads. In summary, NAPI is a technique for network device drivers to improve Linux performance under heavy network traffic by reducing interrupt processing and using polling.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimal Repeated Frame Compensation Using Efficient Video CodingIOSR Journals
1) The document proposes a new video coding standard called Optimal Repeated Frame Compensation (ORFC) which aims to improve compression efficiency. ORFC works by combining repeated frames in a video sequence into a single frame to reduce the total number of frames.
2) The method involves segmenting videos into shots and then analyzing frames within each shot to identify repeated frames. Repeated frames are combined using ORFC to extract key frames, minimizing the number of frames needed to represent the video.
3) Experimental results on test video sequences show the method achieves high compression ratios on average of 99.5% while maintaining good fidelity between 0.75 to 0.78 in extracted key frames. The results indicate OR
This document describes a system for Tamil video retrieval based on categorization in the cloud. The system first categorizes Tamil videos into subcategories based on camera motion parameters. It then segments the videos into shots and extracts representative key frames from each shot based on edge and color features. These features are stored in a feature library in the cloud. When a Tamil query is submitted, the system retrieves similar videos from the cloud based on matching the query features to the stored features. The system is implemented using the Eucalyptus cloud computing platform for its flexibility and ability to handle large computational loads.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
1) The document proposes a method for tracking moving objects in videos captured using a moving camera in complex scenes. It involves video stabilization, key frame extraction, object detection/tracking using Gaussian mixture models and Kalman filters, and object recognition using bag of features.
2) Key frame extraction identifies important frames for processing by computing edge differences between frames and selecting frames above a threshold.
3) Moving objects are detected using background subtraction and Gaussian mixture models, and then tracked across frames using Kalman filters.
4) Object recognition is performed using bag of features, which represents objects as histograms of visual word frequencies to classify objects based on characteristic visual parts.
Key Frame Extraction in Video Stream using Two Stage Method with Colour and S...ijtsrd
Key Frame Extraction is the summarization of videos for different applications like video object recognition and classification, video retrieval and archival and surveillance is an active research area in computer vision. In this paper describe a new criterion for well presentative key frames and correspondingly, create a key frame selection algorithm based Two stage Method. A two stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Khaing Thazin Min | Wit Yee Swe | Yi Yi Aung | Khin Chan Myae Zin "Key Frame Extraction in Video Stream using Two-Stage Method with Colour and Structure" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27971.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-processing/27971/key-frame-extraction-in-video-stream-using-two-stage-method-with-colour-and-structure/khaing-thazin-min
IRJET-Feature Extraction from Video Data for Indexing and Retrieval IRJET Journal
This document summarizes techniques for feature extraction from video data to enable effective indexing and retrieval of video content. It discusses common approaches for segmenting video into shots and scenes, extracting key frames, and determining various visual features like color, texture, objects and motion. Feature extraction is an important but time-consuming step in content-based video retrieval. The document also reviews methods for video representation, mining patterns from video data, classifying video content, and generating semantic annotations to support search and retrieval of relevant videos.
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
The document presents a novel method for extracting key frames from videos using unsupervised clustering and mutual comparison. It assigns weights of 70% to color (HSV histogram) and 30% to texture (GLCM) when computing frame similarity for clustering. It then performs mutual comparison of extracted key frames to remove near duplicates, improving accuracy. The algorithm is computationally simple and able to detect unique key frames, improving concept detection performance as validated on open databases.
IRJET- Study of SVM and CNN in Semantic Concept DetectionIRJET Journal
1) The document discusses approaches for semantic concept detection in videos using techniques like support vector machines (SVM) and convolutional neural networks (CNN).
2) It proposes a concept detection system that uses SVM and CNN together, extracting features from key frames using Hue moments and classifying the features with SVM and CNN.
3) The outputs of SVM and CNN are fused to improve concept detection accuracy compared to using the classifiers individually. Fusing the two classifiers is intended to better identify the concepts in video frames.
IRJET - Applications of Image and Video Deduplication: A SurveyIRJET Journal
This document discusses applications of image and video deduplication techniques. It begins by providing background on the growth of multimedia data and need for deduplication to reduce redundant data. It then describes key aspects of image and video deduplication, including extracting fingerprints from images and frames to identify duplicates. The document reviews several studies on image and video deduplication applications, such as identifying near-duplicate images on social media, detecting spoofed face images, verifying image copy detection, and eliminating near-duplicates from visual sensor networks. Overall, the document surveys various real-world implementations of image and video deduplication.
Dynamic Threshold in Clip Analysis and RetrievalCSCJournals
Key frame extraction can be helpful in video summarization, analysis, indexing, browsing, and retrieval. Clip analysis of key frame sequences is an open research issues. The paper deals with identification and extraction of key frames using dynamic threshold followed by video retrieval. The number of key frames to be extracted for each shot depends on the activity details of the shot. This system uses the statistics of comparison between the successive frames within a level extracted on the basis of color histograms and dynamic threshold. Two program interfaces are linked for clip analysis and video indexing and retrieval using entropy. The results using proposed system on few video sequences are tested and the extracted key frames and retrieved results are shown.
IRJET- Storage Optimization of Video Surveillance from CCTV CameraIRJET Journal
This document proposes a method to optimize storage space occupied by CCTV video footage. It divides video sequences into frames and compares adjacent frames using MSE (mean squared error) to identify redundant frames. Redundant frames with an MSE below a threshold are deleted. This reduces the number of frames stored while maintaining video quality. The proposed method is tested on a sample 20 minute, 110MB video and reduces its size by 30.91% to 76MB and duration to 7 minutes by removing redundant frames. This storage optimization technique is useful for managing the large amounts of data generated daily by CCTV cameras.
The document summarizes a research paper that proposes a method to summarize parking surveillance footage. The method first pre-processes the raw footage to extract only frames containing vehicles. These frames are then classified using a CNN model to detect vehicles and recognize license plates. The classified objects and license plate numbers are used to generate a textual summary of the vehicles in the footage, making it easier for users to review large amounts of surveillance video. The paper discusses related work on video summarization techniques and provides details of the proposed methodology, which includes preprocessing footage, extracting features from frames containing vehicles, using CNNs for object detection and license plate recognition, and generating a summarized video and text report.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database based on motion detection. Features are extracted from these regions and used for matching to recognize the genre. A tree pruning technique is employed to identify the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, recognizes them, and uses tree pruning for genre recognition and retrieval. It was evaluated on a dataset containing sports, movies, and news genres and showed effectiveness in genre recognition and retrieval.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database. Features are extracted from these regions and used for matching via Euclidean distance. A tree pruning technique is employed to recognize the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, extracts features, performs matching and genre recognition, and retrieves relevant videos in three or fewer sentences.
Motion detection in compressed video using macroblock classificationacijjournal
n this paper, to detect the moving objects between frames in compressed video and to obtain the bes
t
compression video
and the noiseless video. We describe a video in which frames by classifying
macroblocks (MB), and describe motion estimation (ME), motion vector field (MV) and motion
compensation (MC). we propose to classify Macroblocks of each video frame into different
classes and use
this class information to describe the frame content based on the motion vector. MB class informatio
n
video applications such as shot change detection, motion discontinuity detection, Outlier rejection
for
global motion estimation. To reduc
e the noise and to improve the clarity of the compressed video by using
contrast limited adaptive histogram equalization (CLAHE) Algorithm.
VIDEO SEGMENTATION & SUMMARIZATION USING MODIFIED GENETIC ALGORITHMijcsa
Video summarization of the segmented video is an essential process for video thumbnails, video
surveillance and video downloading. Summarization deals with extracting few frames from each scene and
creating a summary video which explains all course of action of full video with in short duration of time.
The proposed research work discusses about the segmentation and summarization of the frames. A genetic
algorithm (GA) for segmentation and summarization is required to view the highlight of an event by
selecting few important frames required. The GA is modified to select only key frames for summarization
and the comparison of modified GA is done with the GA.
VIDEO SEGMENTATION & SUMMARIZATION USING MODIFIED GENETIC ALGORITHMijcsa
Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading. Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time. The proposed research work discusses about the segmentation and summarization of the frames. A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required. The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.
VIDEO SEGMENTATION & SUMMARIZATION USING MODIFIED GENETIC ALGORITHMijcsa
Video summarization of the segmented video is an essential process for video thumbnails, video
surveillance and video downloading. Summarization deals with extracting few frames from each scene and
creating a summary video which explains all course of action of full video with in short duration of time.
The proposed research work discusses about the segmentation and summarization of the frames. A genetic
algorithm (GA) for segmentation and summarization is required to view the highlight of an event by
selecting few important frames required. The GA is modified to select only key frames for summarization
and the comparison of modified GA is done with the GA.
VISUAL ATTENTION BASED KEYFRAMES EXTRACTION AND VIDEO SUMMARIZATIONcscpconf
Recent developments in digital video and drastic increase of internet use have increased the
amount of people searching and watching videos online. In order to make the search of the
videos easy, Summary of the video may be provided along with each video. The video summary
provided thus should be effective so that the user would come to know the content of the video
without having to watch it fully. The summary produced should consists of the key frames that
effectively express the content and context of the video. This work suggests a method to extract
key frames which express most of the information in the video. This is achieved by quantifying
Visual attention each frame commands. Visual attention of each frame is quantified using a
descriptor called Attention quantifier. This quantification of visual attention is based on the
human attention mechanism that indicates color conspicuousness and the motion involved seek
more attention. So based on the color conspicuousness and the motion involved each frame is
given a Attention parameter. Based on the attention quantifier value the key frames are extracted and are summarized adaptively. This framework suggests a method to produces meaningful video summary.
Video Content Identification using Video Signature: SurveyIRJET Journal
This document summarizes previous research on video content identification using video signatures. It discusses three types of video signatures (spatial, temporal, and spatio-temporal) that have been used to generate unique descriptors to identify identical video scenes. The document then reviews several existing methods for video signature extraction and matching, including techniques based on ordinal signatures, motion signatures, color histograms, local descriptors using interest points, and compressed video shot matching using dominant color profiles. It concludes by proposing a new temporal signature-based method that aims to accurately detect a video segment embedded in a longer unrelated video by extracting frame-level features, generating fine and coarse signatures, and performing frame-by-frame signature matching.
This document proposes a method for video copy detection using segmentation, MPEG-7 descriptors, and graph-based sequence matching. It extracts key frames from videos, extracts features from the frames using descriptors like CEDD, FCTH, SCD, EHD and CLD, and stores them in a database. When a query video is input, its features are extracted and compared to the database to detect if it matches any videos already in the database. Graph-based sequence matching is also used to find the optimal matching between video sequences despite transformations like changed frame rates or ordering. The method is shown to perform better than previous techniques at detecting copied videos through transformations.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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1. M. A. A Victoria et al. Int. Journal of Engineering Research and Application ww.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
www.ijera.com 446 | P a g e
Discriminative Feature Based Algorithm for Detecting And
Classifying Frames In Image Sequences
M. Antony Arockia Victoria, R. Sahaya Jeya Sutha
B.E,M.E. Assistant Professor, Department of MCA, Dr.Sivanthi Aditanar College of Engineering,
MCA,M.Phil. Assistant Professor, Department of MCA, Dr. Sivanthi Aditanar College of Engineering
ABSTRACT
This method is used to detect and classify frames in different videos. By detecting frames similar video as input
video and related videos are retrieved. Content Based Copy Detection method used to find content related
frames from multiple shots. To improve the efficiency of Content Based Copy Detection the videos are cropped
that means removing the black bars from horizontal and vertical position. These cropped videos are robust to
cam cording and encoded video. Affinity Propagation and exemplar based clustering used to reduce the number
of frames in each video. In Exemplar based clustering unique frames are selected from multiple shots and
Affinity Propagation used to cluster the unique frames. So it will be useful in detect the frames and compare the
input video with all frames. Affinity Propagation uses different similarity metrics to detect the difference
between two frames or two videos. Frame classification results are achieved by tiny videos when compared with
the tiny images framework. Therefore video frames convert into low dimensional resentation that means resize
the frames Frames into 32*32 pixels and concatenating the color channels to reduce the sensitivity in variation.
Simple data mining technique that is nearest neighbor method to perform related video retrieval and frame
classification. This method can be effectively used for recognizing research that is video as same as to input
video will be retrieved and related videos also retrieved.
I. INTRODUCTION
Videos are collected from YouTube’s News
Sports, People, Travel and technology section. The
tiny video database are contains preprocessed vide,
the videos are collected from YouTube and fed into
some preprocessing steps. first, split video into
frames then remove black bars from horizontal and
vertical position second step is resize these frames
into 32*32 pixels and convert into low dimensional
conversion, using this LUV conversion variation
sensitivity can be reduced, after that with the help of
Affinity Propagation unique looking frames are
identified from each video AP Sampling discard all
similar frames and retained only unique looking
frame. This method can be useful while content based
copy detection. Here content based copy detection
Used to identify the video as same as input video and
related video of input video.
There are main advantages in frame splitting
and classification. First, it improves accuracy of
related and duplicate frame identification because
each and every frame is compared with Input video
frame.
Content based copy detection used to detect
the same video shots occurring in different.
Classification results achieved by tiny videos are
compared with tiny image dataset.
Here frames are resized like tiny image so
that tiny frames are also compatible with tiny image
dataset. This image dataset can be useful in
classifying frames into broad category. Same
descriptor can be used for tiny video database and
tiny image database. To retrieve related video, simple
data mining techniques used that are nearest neighbor
method it reduces complexity.
Content Based Copy Detection schemes
appeared as an alternative to the watermarking
approach for persistent identification of images and
of video clips.CBCD approach only uses content
RESEARCH ARTICLE OPEN ACCESS
2. M. A. A Victoria et al. Int. Journal of Engineering Research and Application ww.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
www.ijera.com 447 | P a g e
based comparison between the original video stream
and the controlled one. For storage and
computational considerations, it generally consists in
extracting as few features as possible from the video
stream and matching them with the database. Content
Based Copy Detection presents two major
advantages. First, a video clip which has already been
distributed can be recognized. Secondly, content
based features are intrinsically more robust than
inserted ones because they contain information.
II. EXISTING SYSTEM
To obtain such a large amount of data, they
download 80 million images from the web. The
images are re-sized to 32*32 pixels before they are
added to the dataset. These 80 million tiny images are
used to perform object and scene recognition, object
localization, image orientation detection, and image
colorization. To detect and classify production effect
in video sequences a feature based algorithm is
used.cuts, fades, dissolves, wipes and caption are
detected by this method. The most common
production effects are scene break which mark
transition from one sequence of consecutive images
to another. Cut is an instantaneous transition from
one scene to next. A fade is a gradual transition
between a scene and a constant image (fade-out) or
between a constant image and a scene (fade-in).A
dissolve is a gradual transition from one scene to
another, in which first scene fades out and the second
scene fades in. Another common scene break is wipe
in which a line moves across the screen, in this paper
we can detect and classifying frames in image
sequence. Tiny videos better suited for classifying
scenery and sports related activities while tiny image
perform better at recognizing object.
A large number of video summarization
algorithms have been developed to perform temporal
compression [5],[10]. In uniform sampling, frames
are extracted at constant interval. The main
advantage of this approach is computational
efficiency. However, uniform sampling tends to
oversample long shots or skip short shots. Another
method is intensity of motion sampling, Intensity of
motion has also been used as feature vector for
describing motion characteristics. Intensity motion is
defined as the mean of consecutive frame differences.
A(t)=1/xy∑│L(x,y,t+1)-L(x,y,t)│
Where X and Y are dimensions of the video and
L(x,y,t) denotes the luminance value of pixel (x,y) of
a frame at a time t.Intensity of Motion key frame
selection algorithm is robust to color and affine
transformations. These properties make it suitable for
Content based copy detection.
III. PROPOSED SYSTEM
Our aim is to obtain a similar dataset from
videos, and to explore its applications to video
retrieval and recognition. In this paper we proposed a
new summarization algorithm that uses exemplar
based clustering to select only unique looking key
frames. Exemplar based clustering not only captures
within visual appearance variations, but also
consolidates similarity across multiple shots. In this
paper affinity propagation algorithm used to cluster
densely sampled frames into visually related groups.
Only the exemplar frame within each cluster is
retained and rests are discarded. AP sampling is
particularly suitable for define what “unique
Bar removal
Frame splitting
LUV conversion
Key
frame
32*32
Meaningful frames
tiny video search
Retrieval
classification
Fig. 2. System Architecture
Looking” means in terms of the same frame
similarity metrics that for video retrieval.
Similarities between two frames or two
videos are defined by basic distance between two tiny
images Ia and Ib as their sum of squared difference;
D2
ssd(Ia,Ib)=∑x,y,z (Ia(x,y,c)-Ib(x,y,c))2
Where I denotes a 32*32*3 dimensional zero mean,
normalized tiny video frame or tiny image. We show
that recognition performance can be improved by
allowing the pixels of the tiny image to shift slightly
within a 5-pixels window.
IV. TINY VIDEO REPRESENTATION
A) Video Collection
The Videos were primarily collected in
YouTube’s News, Sports, People, Travel and
Technology. For each video, we also store all of
associated metadata returned by YouTube API. The
video
Preprocess
ed content
Splitted
frames
Low
dimensional
frame
Intensity
motion
sample
Exampler
based
clustering
Tiny
video
database
Complete
sampled
Input
video
Related
videos
Classified
video
3. M. A. A Victoria et al. Int. Journal of Engineering Research and Application ww.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
www.ijera.com 448 | P a g e
metadata includes such information as video
duration, rating, view count, title, descriptions and
assigned label.
B) Video Preprocessing Procedure
In this paper videos are preprocessed that is
remove black bars from horizontal and vertical
position using following formula
F(y) = 1/M ∑x,c │Iy(x,y,c)│,
Ymin = min[argy min (f(y)>t)],
Ymax = max[argy max (f(y)>t] .
Where Iy is the derivative along y of frame I, which
is an M*N*3 matrix. Remove frames that contain
more than 80% of pixels of the same color.
Fig. 2. Removal of black bars
(See Fig 2)The region above ymin and below ymax is
only cropped if they contain at least 80 percent black
pixels. Fig. c. shows after horizontal and vertical
crop.
C) Low Dimensional Video Representation
In this paper Video frames are resized into
32*32 pixels and three color channels are
concatenated. This normalized tiny frames are ready
to compatible with tiny image frame work, this will
improve classification result. A large number of
video summarization algorithms have been developed
to perform temporal compression [5],[10]. In uniform
sampling, frames are extracted at constant interval.
The main advantage of this approach is
computational efficiency. However, uniform
sampling tends to oversample long shots or skip short
shots. Another method is intensity of motion
sampling, Intensity of motion has also been used as
feature vector for describing motion characteristics.
Intensity motion is defined as the mean of
consecutive frame differences.
A(t)=1/xy∑│L(x,y,t+1)-L(x,y,t)│
Where X and Y are dimensions of the video and
L(x,y,t) denotes the luminance value of pixel (x,y) of
a frame at a time t. Intensity of Motion key frame
selection algorithm is robust to color and affine
transformations. These properties make it suitable for
Content based copy detection.
Fig. 3. Intensity of motion plots with Gaussion filters
applied. (a) x= 10 (b) x= 30.
In this paper we proposed a new summarization
algorithm that uses exemplar based clustering to
select only unique looking key frames. Exemplar
based clustering not only captures within visual
appearance variations, but also consolidates
similarity across multiple shots. In this paper affinity
propagation algorithm used to cluster densely
sampled frames into visually related groups. Only the
exemplar frame within each cluster is retained and
rests are discarded. AP sampling is particularly
suitable for define what “unique looking” means in
terms of the same frame similarity metrics that for
video retrieval.
Fig. 4. Comparison of (a) uniform sampling to (b)
AP sampling.
The green samples are of a scene that was
already sampled once. But the blue samples are not
present in uniform sampling. The Red samples are
content missing scene from AP sampling.
D) Frame and Video Similarity Metrics
Similarity between two frames or two videos
are defined by basic distance between two tiny
images Ia and Ib as their sum of squared difference;
D2
ssd(Ia,Ib)=∑x,y,z (Ia(x,y,c)-Ib(x,y,c))2
Where I denotes a 32*32*3 dimensional
zero mean, normalized tiny video frame or tiny
image. We show that recognition performance can be
improved by allowing the pixels of the tiny image to
shift slightly within a 5-pixels window.
D2
shift(Ia,Ib)=∑x,y,cmin│Dx,y│≤w(Ia(x,y,c)
Ib(x+Dx,y+Dy,c))2
V. CONTENT BASED COPY
DETECTION
The aim is to detect the same video shots
occurring in different video. our preprocessing steps
coupled with the small size of tiny video frame make
our descriptors and similarity metrics robust to cam
cording, strong recoding, subtitles and mirroring
transformation.
4. M. A. A Victoria et al. Int. Journal of Engineering Research and Application ww.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
www.ijera.com 449 | P a g e
A) Related Video Retrieval Using Tiny Videos
To find YouTube videos those are related by
content (that is sharing at least one duplicate shot)
and to evaluate frequency of such occurrences.
Precision is defined as the fraction of videos
identified as containing duplicate shots. Recall
indicates a fraction of related videos found out of all
videos with a duplicate shot.
VI. VIDEO CATEGORIZATION
In this paper large data base of videos to
classify unlabeled images and video frames into
broad categories. We compare our classification
results with tiny images.
A) Labeling noise
The label for an image could originate from
surrounding text which does always describe the
image’s content. This means that each tiny image is
loosely tied to its label. Tags assigned by user with a
specific goal of describing the content of videos. A
label for video could only apply to a specific segment
of videos and completely unrelated to other parts.
Therefore many video frames in the tiny video are
unrelated to videos label since the user does not
indicate which labels apply to which part of the
video.
B) Classification based on WorldNet Voting
To reduce the labeling noise, we use word
net voting scheme. Our goal is to classify “person
image”, then not only do the neighbors labeled with
the person tag vote for the category
(E.g. politician, scientist and so on).Videos in our
dataset very frequently have more than one label
(tag). To ensure that a video with multiple tags gets
the same vote as a video’s vote evenly across all of
its tags. Finally, tiny images and tiny videos can be
combined in order to improve precision for some
categorization tasks.
C) Categorization result
In this paper, we evaluate classification
performance for tiny image, tiny videos and both
datasets combined. The man made device
categorization task includes positive examples of
mobile phones, computers, and other technical
equipment. For people, we use number of votes for
the person noun in the WorldNet tree. The noun
“person” has children such as
“politician”,”chemist”,”reporter”,”child”.while
negative examples contain no people in them.
D) Alternate Video Similarity Metric for
Categorization
Videos with a single very similar frame key
frame and multiple completely dissimilar key frames
tend to make better neighbors in classifying an input
image than videos with multiple moderately similar
key frames. This is the case because our AP sampling
algorithm picks exemplar key frames and discards all
other similar looking frames.
D2
shift of the closest frame in video V to our
unlabeled input image Ia.,..we can determine different
distance measure which returns an average distance
of the n-closest frames I(1…n) in video V for an input
image Ia.
n
D2
n(I, V)= 1/n∑(D2
shift(Ia,Ib))
b=1
the average distance of n closest frames is simply the
distance to the closest frame.
E) Classification Using YouTube Categories
Tiny video dataset contains metadata
provided by YouTube for each video. Unlike tiny
image dataset, which has only one label per image,
tiny video stores the video’s title, description, rating,
view count, a list of related videos and other
metadata.
All videos on YouTube are placed into a
category. The number of categories on YouTube has
increased over the years. Currently videos on
YouTube can appear in one of 14 categories. Tiny
video dataset contains videos from News Sports,
People, Technology, Travel categories.
VII. EXPERIMENTAL RESULTS
For our experimental evaluation,we use
Matlab, we can understand the performance of tiny
video dataset frame classification and retrieval. Fig. 5
shows false positive and false negative results.
Fig. 5. ROC Curve
Unique frames are clustered using affinity
Propagation sampling. Fig. 6. Shows the result of AP
sampling.
5. M. A. A Victoria et al. Int. Journal of Engineering Research and Application ww.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.446-450
www.ijera.com 450 | P a g e
Fig. 6. AP sampling
VIII. CONCLUSION
This paper presents a method for
compressing large dataset of videos into compact
representation called tiny video. It shows tiny videos
can be effectively for content based copy detection.
Simple nearest neighbor method used to perform
variety of classification tasks. In this paper tiny video
dataset designed to compatible with tiny image to
improve classification precision and recall. Finally
additional metadata in the tiny video database can be
used to improve classification precision for some
category. The same descriptor used for tiny videos
and tiny image. This allows combining two dataset
for classification. However RGB color space not used
for content based copy detection so in this paper three
color channels are concatenated and CBCD method
used.
REFERENCES
[1] Aarabi.P and Karpenko.A , (2008 ) ‘Tiny
Videos: Non-Parametric Content-Based
Video Retrieval and Recognition,’ Proc.
Tenth IEEE Int’l Symp. Multimedia, pp. 619-
624, Dec.
[2] Alexandre Karpenko and Parham Aarabi
(2011), ’Tiny Video:A Large Dataset For
Nonparametric Video Retrieval and Frame
Classification’,IEEE Transaction on pattern
Analysis and Machine Intelligence,vol
33,No.3.
[3] Buisson.O ,Boujemaa.N , Brunet.V , Chen.L
, Gouet , Joly.A , Law-To.J, Laptev.I and
Stentiford.F, (2007) ‘Video Copy Detection:
A Comparative Study,’ Proc. Sixth ACM
Int’l Conf. Image and Video Retrieval, pp.
371-378.
[4] Buisson,O , Fre´licot.C and Joly.A, (2003)
‘Robust Content-Based Video Copy
Identification in a Large Reference
Database,’ Proc. Conf. Image and Video
Retrieval, pp. 414-424.
[5] Das.M, Liou.S.P, Toklu.C, (2000) ‘Video
abstract: A Hybrid Approach to Generate
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[6] Freeman.W.T, Fergus and Torralba,A, (2007)
‘80 Million Tiny Images: A Large Data Set
for Non-Parametric Object and Scene
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[7] Miller’s, Mai.K and Zabih.R, (1995) ‘A
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ABOUT THE AUTHORS
M.Antony Arockia Victoria She is a
Assistant Professor in Dr. Sivanthi
Aditanar College of Engineering,
Tiruchendur. She has done her M.E (CSE)
in Dr.Pauls Engineering College, Anna University
@Chennai in 2012. She received her B.E degree from
Raja College of Engineering & Technlogy. Anna
University @ Chennai in 2010. She had presented
papers in national and International Conferences.
R.Sahaya Jeya Sutha She is presently
working as a Assistant Professor of MCA
department in Dr. Sivanthi Aditanar
College of Engineering, Tiruchendur. She
worked as a Academic Counsellor for 5
years in IGNOU, Tuticorin Regional Centre. She has
nearly 5 years of experience in fulltime teaching, 6
years part-time teaching and 13 years in Industry.
She has done her B.Sc(CS),MCA,M.Phil under
Manonmaniam Sundaranar University, Tirunelveli.
She developed various software for her Institution
while working as a Programmer. She delivered guest
lectures in other colleges. She has also organized
workshops and conferences. She acted as a
coordinator for department association and Computer
Education. Her area of interest is Digital Image
Processing. She is a life member of Indian Society
for Technical education ISTE. She had attended
number of Seminars, Workshops, Faculty
Development Programme and Conferences.