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.
DIGITAL VIDEO HOLOGRAPHY- A ROBUST TOOL FOR COMMUNICATIONcscpconf
Holograms are being produced using optical methods for decades. A lot of techniques and
methods exist for the production of efficient holograms. Digital Holography (DH) is
the method of simulating holograms with the use of computer. In this paper digital holograms
are generated using Fresnel and Fraunhofer diffraction integrals. Multi color holograms are
simulated and the digitally generated holograms are analysed. DH technique is extended
further to video format which yields video holograms. The concept that every bit of a hologram
contains full information of the original video, which is being effectively utilized to reduce the
file size required for communication in terms of storage, security and speed. The entire process
is simulated using Matlab7.10 environment.
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.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Video Shot Boundary Detection Using The Scale Invariant Feature Transform and...IJECEIAES
Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.
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.
DIGITAL VIDEO HOLOGRAPHY- A ROBUST TOOL FOR COMMUNICATIONcscpconf
Holograms are being produced using optical methods for decades. A lot of techniques and
methods exist for the production of efficient holograms. Digital Holography (DH) is
the method of simulating holograms with the use of computer. In this paper digital holograms
are generated using Fresnel and Fraunhofer diffraction integrals. Multi color holograms are
simulated and the digitally generated holograms are analysed. DH technique is extended
further to video format which yields video holograms. The concept that every bit of a hologram
contains full information of the original video, which is being effectively utilized to reduce the
file size required for communication in terms of storage, security and speed. The entire process
is simulated using Matlab7.10 environment.
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.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Video Shot Boundary Detection Using The Scale Invariant Feature Transform and...IJECEIAES
Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative...Dibya Jyoti Bora
Multispectral satellite color images need special treatment for object-based classification like segmentation.
Traditional algorithms are not efficient enough for performing segmentation of such high-resolution images as
they often result in a serious problem: over-segmentation. So, an innovative approach for segmentation of
multispectral color images is proposed in this paper to tackle the same. The proposed approach consists of two
phases. In the first phase, the pre-processing of the selected bands is conducted for noise removal and contrast
enhancement of the input multispectral satellite color image on the HSV color space. In the second phase, fuzzy
segmentation of the enhanced version of the image obtained in the first phase is carried out by FCM algorithm
through optimal parameter passing. Final shifting from HSV to RGB color space presents the segmentation
result by separating different regions of interest with proper and distinguished color labeling. The results found
are quite promising and comparatively better than the other state of the art algorithms.
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.
THE REDUCTION IN COMPUTATION TIME IN THE COLOR IMAGE DECOMPOSITION WITH THE B...IJCSEIT Journal
This paper presents two different approaches in color image decomposition domain with Bidimensional
Empirical Mode Decomposition (BEMD). The first approach applies the BEMD on each channel
separately and the second is based on interpolation of each channel in the sifting process. The comparison
of two approaches shows the same performance of each approach in terms of visual quality, but they do not
provide the same results in execution time which presents the most important criteron in real time
applications. It was shown that the second BEMD approach based on interpolation of each channel in the
sifting process, gives a gain in the point of view the execution time.
DETECTING DOUBLY COMPRESSED H.264/AVC VIDEOS USING MARKOV STATISTICS SCHEMEMichael George
MPEG-4 is a method of defining compression of audio and video digital data. MPEG-4 videos double compression method is used to decompress video from spatial domain to the frequency domain and then resaved with a different quantization matrix. Markov feature is a second order statistics which was adopted to detect double compression artifacts in the original video. In proposed system, double compression artifacts in H.264/AVC was detected by Markov statistics and by comparing two videos the ratios such as peak signal- to- noise ratio (PSNR), mean squared error (MSE) and structural similarity index (SSIM) are calculated.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
Repeat-Frame Selection Algorithm for Frame Rate Video TranscodingCSCJournals
To realize frame rate transcoding, the forward frame repeat mechanism is usually adopted to compensate the skipped frames in the video decoder for end-user. However, based on our observation, it is unsuitable for repeating all skipped frames only in the forward direction and sometimes the backward repeat may achieve better results. To deal with this issue, we propose the new reference frame selection method to determine the direction of repeat-frame for skipped Predictive (P) and Bidirectional (B) frames. For P-frame, the non-zero transformed coefficients and the magnitude of motion vectors are taken into consideration to determine the use of forward or backward repeat. For B-frame, the magnitude of motion vector and its corresponding reference directions of the blocks in B-frame are selected to be the decision criteria. Experimental results show that the proposed method provides 1.34 dB and 1.31 dB PSNR improvements in average for P and B frames, respectively, compared with forward frame repeat.
Key frame extraction for video summarization using motion activity descriptorseSAT Journals
Abstract Summarization of a video involves providing a gist of the entire video without affecting the semantics of the video. This has been implemented by the use of motion activity descriptors which generate relative motion between consecutive frames. Correctly capturing the motion in a video leads to the identification of the key frames in the video. This motion in the video can be obtained by using block matching techniques which is an important part of this process. It is implemented using two techniques, Diamond Search and Three Step Search, which have been studied and compared. The comparison process is tried across various videos differing in category, content, and objects. It is found that there is a trade-off between summarization factor and precision during the summarization process. Keywords: Video Summarization, Motion Descriptors, Block Matching
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
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative...Dibya Jyoti Bora
Multispectral satellite color images need special treatment for object-based classification like segmentation.
Traditional algorithms are not efficient enough for performing segmentation of such high-resolution images as
they often result in a serious problem: over-segmentation. So, an innovative approach for segmentation of
multispectral color images is proposed in this paper to tackle the same. The proposed approach consists of two
phases. In the first phase, the pre-processing of the selected bands is conducted for noise removal and contrast
enhancement of the input multispectral satellite color image on the HSV color space. In the second phase, fuzzy
segmentation of the enhanced version of the image obtained in the first phase is carried out by FCM algorithm
through optimal parameter passing. Final shifting from HSV to RGB color space presents the segmentation
result by separating different regions of interest with proper and distinguished color labeling. The results found
are quite promising and comparatively better than the other state of the art algorithms.
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.
THE REDUCTION IN COMPUTATION TIME IN THE COLOR IMAGE DECOMPOSITION WITH THE B...IJCSEIT Journal
This paper presents two different approaches in color image decomposition domain with Bidimensional
Empirical Mode Decomposition (BEMD). The first approach applies the BEMD on each channel
separately and the second is based on interpolation of each channel in the sifting process. The comparison
of two approaches shows the same performance of each approach in terms of visual quality, but they do not
provide the same results in execution time which presents the most important criteron in real time
applications. It was shown that the second BEMD approach based on interpolation of each channel in the
sifting process, gives a gain in the point of view the execution time.
DETECTING DOUBLY COMPRESSED H.264/AVC VIDEOS USING MARKOV STATISTICS SCHEMEMichael George
MPEG-4 is a method of defining compression of audio and video digital data. MPEG-4 videos double compression method is used to decompress video from spatial domain to the frequency domain and then resaved with a different quantization matrix. Markov feature is a second order statistics which was adopted to detect double compression artifacts in the original video. In proposed system, double compression artifacts in H.264/AVC was detected by Markov statistics and by comparing two videos the ratios such as peak signal- to- noise ratio (PSNR), mean squared error (MSE) and structural similarity index (SSIM) are calculated.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
Repeat-Frame Selection Algorithm for Frame Rate Video TranscodingCSCJournals
To realize frame rate transcoding, the forward frame repeat mechanism is usually adopted to compensate the skipped frames in the video decoder for end-user. However, based on our observation, it is unsuitable for repeating all skipped frames only in the forward direction and sometimes the backward repeat may achieve better results. To deal with this issue, we propose the new reference frame selection method to determine the direction of repeat-frame for skipped Predictive (P) and Bidirectional (B) frames. For P-frame, the non-zero transformed coefficients and the magnitude of motion vectors are taken into consideration to determine the use of forward or backward repeat. For B-frame, the magnitude of motion vector and its corresponding reference directions of the blocks in B-frame are selected to be the decision criteria. Experimental results show that the proposed method provides 1.34 dB and 1.31 dB PSNR improvements in average for P and B frames, respectively, compared with forward frame repeat.
Key frame extraction for video summarization using motion activity descriptorseSAT Journals
Abstract Summarization of a video involves providing a gist of the entire video without affecting the semantics of the video. This has been implemented by the use of motion activity descriptors which generate relative motion between consecutive frames. Correctly capturing the motion in a video leads to the identification of the key frames in the video. This motion in the video can be obtained by using block matching techniques which is an important part of this process. It is implemented using two techniques, Diamond Search and Three Step Search, which have been studied and compared. The comparison process is tried across various videos differing in category, content, and objects. It is found that there is a trade-off between summarization factor and precision during the summarization process. Keywords: Video Summarization, Motion Descriptors, Block Matching
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
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
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
Key-frame extraction is one of the important steps in semantic concept based video indexing and retrieval and accuracy of video concept detection highly depends on the effectiveness of keyframe extraction method. Therefore, extracting key-frames efficiently and effectively from video shots is considered to be a very challenging research problem in video retrieval systems. One of many approaches to extract key-frames from a shot is to make use of unsupervised clustering. Depending on the salient content of the shot and results of clustering, key-frames can be extracted. But usually, because of the visual complexity and/or the content of the video shot, we tend to get near duplicate or repetitive key-frames having the same semantic content in the output and hence accuracy of key-frame extraction decreases. In an attempt to improve accuracy, we proposed a novel key-frame extraction method based on unsupervised clustering and mutual comparison where we assigned 70% weightage to color component (HSV histogram) and 30% to texture (GLCM), while computing a combined frame similarity index used for clustering. We suggested a mutual comparison of the key-frames extracted from the output of the clustering where each key-frame is compared with every other to remove near duplicate keyframes. The proposed algorithm is both computationally simple and able to detect non-redundant and unique key-frames for the shot and as a result improving concept detection rate. The efficiency and effectiveness are validated by open database videos.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
In this paper, we propose a method for effectively tracking moving objects in videos captured using a
moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and
illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized
using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational
time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object
recognition using Bag of features is performed in order to recognize the moving objects.
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the-state-of-the-art algorithms achieved 8.90 in the best case.
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...IJCSEIT Journal
A video fingerprint is a recognizer that is derived from a piece of video content. The video fingerprinting
methods obtain unique features of a video that differentiates one video clip from another. It aims to identify
whether a query video segment is a copy of video from the video database or not based on the signature of
the video. It is difficult to find whether a video is a copied video or a similar video, since the features of the
content are very similar from one video to the other. The main focus of this paper is to detect that the query
video is present in the video database with robustness depending on the content of video and also by fast
search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithms are adopted in
this paper to achieve robust, fast, efficient and accurate video copy detection. As a first step, the
Fingerprint Extraction algorithm is employed which extracts a fingerprint through the features from the
image content of video. The images are represented as Temporally Informative Representative Images
(TIRI). Then, the second step is to find the presence of copy of a query video in a video database, in which
a close match of its fingerprint in the corresponding fingerprint database is searched using inverted-filebased
method. The proposed system is tested against various attacks like noise, brightness, contrast,
rotation and frame drop. Thus the performance of the proposed system on an average shows high true
positive rate of 98% and low false positive rate of 1.3% for different attacks.
VIDEO SUMMARIZATION: CORRELATION FOR SUMMARIZATION AND SUBTRACTION FOR RARE E...Journal For Research
The ever increasing number of surveillance camera networks being deployed all over the world has not only resulted in a high interest in the development of algorithms to automatically analyze the video footage, but has also opened new questions as how to efficiently manage the vast amount of information generated. The user may not have sufficient time to watch the entire video or the whole of video content may not be of interest to the user. In such cases, the user may just want to view the summary of the video instead of watching the whole video. In this paper, we present a video summarization technique developed in order to efficiently access the points of interest in the video footage. The technique aims to eliminate the sequences which contain no activity of significance. The system being developed actually captures each frame from the video, then it processes the frame; if the frame is of its interest, it retains the frames otherwise it discards the frame; hence the resultant video is very short. The proposed method is extended to obtain rare event detection for security systems. These rare event detections refer to suspicious scenarios. The system will consider a particular frame of interest from a video footage taken at given time and search for actions from video footages across the particular area of interest specified by the user. The user is then notified about the objects and actions occurred in the area of interest. This helps in detecting suspicious behavior that would have otherwise been deemed unsuspicious and gone unnoticed in the context of a narrow timeframe.
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.
Video content analysis and retrieval system using video storytelling and inde...IJECEIAES
Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.
Similar to VISUAL ATTENTION BASED KEYFRAMES EXTRACTION AND VIDEO SUMMARIZATION (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
2. 180 Computer Science & Information Technology (CS & IT)
(1)Extracting key frames from the video
(2)Summarizing video by selecting representative keyframes from the extracted potential key
frames.
Key frame extraction refers to finding a set of salient images taken from a full length video that
represent the visual content in the video efficiently. The key frames extracted should be content
based and the process of extraction should be automatic. Previously key frames were extracted
randomly from the video to summarize the video. But randomly extracted frames will not
represent the content of the video and information in the video efficiently. There are various
approaches towards extraction of keyframes. Sampling-based approach is very simple, which
selects key frames by uniformly sampling the frames from original video at fixed time intervals.
The drawback of this approach is that it may cause some important yet short video clips to have
no representative frames while less important but stationary clips could have multiple similar
frames and lead to information redundancy.
The key frames extracted by Segment based approach could represent the video content
efficiently, however, the clustering process is complex and it is difficult to determine the number
of the segments. Shot based techniques are considered to be effective among the three available
approaches as they extract representative keyframes from each shot. Most of the shot based
approaches use low level spatial features like color and intensity histogram. The systems that use
temporal features mostly use a threshold and compare dynamism between two frames. Object
based techniques are efficient and semantic, but it gives more importance to the foreground and
are suitable for certain applications only. Video summarization based on automatic understanding
of semantic video content is far from being achieved.
2. RELATED WORKS
Two basic approaches are used to extract frames from video. They are motion based (activity
based) key frame extraction methods and color based key frame extraction methods. In Color
based method, color histograms are used to measure the similarity among frames. The distance
between the color histogram of each frame is used as similarity measure. But using color
histograms alone, the salient frames cannot be found as they may not capture the dynamics of the
video. Reference [1] Proposes an approach to extract key frames from the video by considering
color distribution with respect to time. It uses an algorithm to find a temporarily maximum
occurrence frame (TMOF) in each shot. The color histogram of each frame is compared with the
Color histogram of the TMOF. By comparing the pixel values at same position through frames
within a shot, a reference frame is constructed with the pixel value throughout the frames in the
same position with the maximum occurrence. The TMOF frame in each shot is the content
descriptive frame of each shot that preserves both the temporal and color information. The
distance measure between histograms of each frame and that of TMOF is calculated. The key
frames are extracted from the peak of the distance curve in [1].
To capture dynamics of the video, TMOF is constructed instead of comparing color histograms of
frames with one another. However, this method succeeds in capturing changes in the visual
(color) content over time. The motion or activity involved is not considered. Amount of motion in
each frame is not measured.
Reference [2] addresses this problem of lack of temporal consistency in the color based
approaches of video summarization by combining both the color and motion based approaches.
By doing so, more meaningful summarization can be achieved. The fundamental idea is to
compute a single image map which is representative of the pictorial content of the video sequence
by warping all the frames contained in it into a reference frame and combining their pixel
3. Computer Science & Information Technology (CS & IT) 181
intensities. The two fundamental video summarization steps are: 1) Fitting a global motion model
to the motion between each pair of successive frames. 2) Computing the summarizing image map
by accumulating the information from all the frames after they have been aligned according to the
motion estimates computed in the previous step. It assumes that there is a dominant motion
among the motions of the objects in a scene and the dominant motion alone is taken into account
blurring the actions of other objects. Considering only the dominant motion need very accurate
implementation techniques, Reference [3] proposes another motion based key frame extraction
technique. It quantifies motion involved in each video segment and accordingly decides the
number of key frames required to represent the video in the summary. The assumption is that,
more the motion in a scene, more key frames is extracted for effective and efficient
summarization. In [3], the video is segmented and the intensity of motion in the video segments is
directly calculated using MPEG-7 motion descriptors. The shots are divided into parts of equal
cumulative motion intensity. Frames located at the centre of each sub segment are selected to
represent the video summary. It also derives an empirical relationship between the motion activity
of the segment and the no. of key frames required to represent them. Reference [4] presents an
idea to capture visual content and the dynamism in the video as well. It uses color based key
frame extraction technique. However, takes activity in the video into account for determining the
no. of key frames to be represented in the summary. Frames are extracted from the video and each
frame is converted from RGB color space to HSV color space and color histograms of all the
frames are constructed. The first frame is stored as key frame and the threshold Ɛ(which is called
dynamic threshold) is chosen. The histograms of consecutive frames are compared with the
histogram of key frame. If the difference exceeds threshold value Ɛ, the corresponding frame is
taken as key frame and stored in a pool. The upcoming frames are then compared with the
histogram of the newly assigned key frame and the same procedure continues on.
Reference [5] extracts key frames based on a three step algorithm. The three major steps are
Preprocessing, Temporal filtering and post processing. Both the first and last steps are done
using queues. In the first step, Entropy based energy minimization method is used to find frames
in the video that has gradual transition. Video frames enter the first queue. Frames that are
insignificant are removed using an energy-minimization method. The output frames of the first
queue are fed into the second queue. Redundant frames are removed in this queue .The frames
that passed both the queues are clustered using dynamic clustering techniques. In the second step,
content dissimilarity in the video is measured. The dissimilarity measure is found by taking
temporal difference between frames. Then the video frames are processed using dynamic
clustering techniques.
The first frame is selected as a cluster. For the sequential frames, if its minimum distance to all
the existing clusters is larger than 5, the frame as a new cluster. Otherwise, it is merged with the
nearest cluster and update the centroid of the cluster. The proposed key frames selection method
is different from the clustering-only method in two ways. First, in this algorithm, the clustering is
a post processing step that refines key frame results. Since the number of video frames is greatly
reduced, this post processing is computationally efficient. Second, the parameter of the clustering
process, 6, is automatically determined based on the queuing process and is adaptive to video
content.
3. PROBLEMS AND MOTIVATION
The various methods of key frame extraction surveyed in the above section helps us to know that
the keyframes are extracted by using either color or motion involved in the video. Some
approaches like [1] and [4] tries to combine both dynamism and the visual content to decide the
salient frames.
4. 182 Computer Science & Information Technology (CS & IT)
However for automatic keyframes extraction using higher level concepts that are in line with
human visual and cognition system, both color and motion should be combined to develop a
criteria based on which salient frames from the video can be extracted. Reference [5] appears to
suggest one such method. In this paper we have implemented the methods suggested by [5] to
extract keyframes from the video and to summarize it.
4. PROPOSED WORK
The proposed system basically extracts key frames and summarizes them, based on a visual
attention model. The system helps achieving a meaningful video summary by bridging the
semantic gap between the low level descriptors used by computer systems and high level
concepts perceived by human based on neurobiological concepts of human perception of vision.
Figure 1. Simplified Architecture of the proposed System
From psychological studies it is known that, human pays more attention to moving objects than a
static object. Not just activities, at times interesting static objects at the back ground which are
visually appealing get the viewer’s attention.So to find the frames that are visually attractive and
meaningful, it becomes essential to model both dynamic and static attention each frame
commands.
Both the static and dynamic attention are quantified using a descriptor called ‘Visual Attention
Index’. Motion intensity and orientation in a video are computed to model dynamic attention the
video commands.VAI of dynamic attention is calculated based on coherence of orientation that is
calculated using Gaussian kernel density estimation. Static attention of each frame is further
calculated based on the Red-Green opponency and blue-Yellow opponency in each frame
4.1 Static Attention Detection Model
The static attention detection module quantifies color conspicuousness of each frame. This model
is based on color opponent theory and works in LMS color space.
LMS is a color space represented by the response of the three types of cones of the human eye,
named after their responsivity (sensitivity) at long, medium and short wavelengths. LMS color
space, represents long, medium, and short light wavelengths (red, green, blue respectively). LMS
values range from -1 to 1. So, black is (-1,-1,-1) and white is (1,1,1), and other colors lie in the
5. Computer Science & Information Technology (CS & IT) 183
range from -1 to 1. sum of L+M is a measure of luminance. The cones in the human are
classified into three classes. These three classes of cones are the short-wavelength sensitive (S-
cones), middle-wavelength sensitive (M-cones) and long-wavelength sensitive (L-cones), and all
have different but overlapping spectral sensitivities.
The color opponent process is a color theory that states that the human visual system interprets
information about color by processing signals from cones and rods in an antagonistic manner. The
opponent color theory suggests that there are three opponent channels: red versus green, blue
versus yellow, and black versus white (achromatic and detects light-dark variation or luminance).
[6] Responses to one color of an opponent channel are antagonistic to those to the other color.
That is, opposite opponent color are never perceived together .There is no "greenish red" or
"yellowish blue". We quantify color conspicuousness of each frame based on Red-Green and
Blue-yellow opponency.Figure2 shows the operation flow of static attention detection module.
The video which is to be summarized is split into individual frames and the individual frames are
given as input into the static Attention detection model.
Figure 2. Operation flow of static attention Module
Each frame is split into 64 blocks that are further operated upon by the algorithm given below to
get the Visual Attention Index of each frame.
The algorithm is as follows:
1 Divide each frame into smaller macro blocks in such a way that each frame is subdivided into
64 blocks.
2 Take one block, at a time. Convert the color space from RGB to LMS
3 RGB to LMS conversion is achieved in two steps
3.1 First RGB to XYZ conversion is carried out.
3.2 Followed by XYZ to LMS conversion
6. 184 Computer Science & Information Technology (CS & IT)
4 RGB to XYZ conversion is done using the transformation matrix
[x y z] = [R G B] 0.5767309 0.2973769 0.0270343
0.1855540 0.6273491 0.0706872
0.1881852 0.0752741 0.9911085
5 Similarly, XYZ to LMS is also achieved through the transformation matrix
[L M S] = [x y z] 0.7328 -0.7036 0.0030
0.4296 1.6975 0.0136
-0.1624 0.0061 0.9834
6 Calculate Red-Green, Blue-Yellow opponency using the following formulae,
Red-Green Opponency = (L-M) / (L+M)
Blue- yellow opponency = (s-0.5*(L+M))/(s+0.5*(L+M)
7 Calculate color contrast and intensity of each block.
8 Calculate center-surround difference of each block using the following formula
d(pi,q)=(0.5 |pi (I)-q(I)| )+ (0.5 |pi (H)-q(H)|
9 Calculate Static Visual attention index as follows:
As = (1/N)* (Σ Wi . Ci)
Where Wi is Gaussian fall off weight.
10 Output the Static Visual Attention Index (VAI) for each frame.
Using the above algorithm, Static Visual Attention index of each frame is calculated. The process
involves conversion of each frame from RGB Color space to LMS color model. Using the LMS
values Centre-surround difference of each block is calculated and then visual attention index of
entire frame is obtained
4.2. Dynamic Attention Detection Module
In the Dynamic attention detection model, the motion associated with each frame of the video is
calculated. Motion estimation is done on the basis that the patterns corresponding to objects and
background in a frame of video sequence move within the frame to form corresponding objects
on the subsequent frame in [7]. The idea behind block matching is to divide the current frame into
a matrix of ‘macro blocks’ that are then compared with corresponding block and its adjacent
neighbors in the previous frame to create a vector that quantifies the movement of a macro block
from one location to another in the previous frame. This movement calculated for all the macro
blocks comprising a frame, constitutes the motion estimated in the current frame. The search area
for a good macro block match is constrained up to p pixels on all fours sides of the corresponding
macro block in previous frame. This ‘p’ is called as the search parameter. The matching of one
macro block with another is based on the output of a cost function. The macro block that results
in the least cost is the one that matches the closest to current block. There are various cost
functions mean absolute difference (MAD) and Mean squared error (MSE).
Based on the above idea the Motion attention detection module is built. Figure 3 shows operation
flow of the Motion attention detection module .The calculation dynamic attention index by
measuring motion associated with each frame is based on the following algorithm:
7. Computer Science & Information Technology (CS & IT) 185
1. Divide the frame into 64 sub-blocks
2. Compare each block of a frame with the corresponding block in the next frame and
calculate the motion vectors dx and dy by Block matching technique.
3. The motion intensity of each block is given by the formula
Motion intensity ϒ i = sqrt (dx i
2
+dyi
2
)
4. The orientation of each block is calculated
Motion orientation ϴi = arctan(dyi /dxi)
5. Then, orientation histogram is built and the motion attention index is calculated from the
following formula:
AT(i) = 1- (v(b(i))/(Σv(j)))
6. Motion Attention of the block is given by
AT (i) = γi AT (i)
AT (i) gives the Motion attention Index of frame i. Motion with high intensity attracts more
attention. Frames with larger attention index are given much importance.
.
Figure 3. Operation flow of Motion Attention detection Model
4.3 Motion Priority Fusion Module
This module fuses the weighted static and dynamic attention indexes of each frame calculated by
following aforesaid modules. The design of the module is given below in Figure 4.
8. 186 Computer Science & Information Technology (CS & IT)
Figure 4. Architecture of Motion Priority Fusion Module
The algorithm applied in Motion priority fusion Module is as follows:
1 Input the Static and Dynamic visual Attention indexes of each frame
2 Define weights of dynamic attention and static attention for each frame using following
formulae.
WT = A’T.exp(1-A’T)
Ws = 1-WT
where A’ T = Max (A T ) – Mean (A T )
3 Calculate final Visual Attention Index
VAI = WsAs + WT AT
4 Output the Total Visual Attention Index
4.4.Adaptive Summarization Module
The video is summarized with the important frames depending on the importance of the frame
determined with the Visual Attention Index of each frame using Adaptive summarization
algorithm. The simplified architecture of the Adaptive summarization module designed and
implemented in this work is shown in figure 5
Figure 5. System Architecture of Adaptive summarization Module
9. Computer Science & Information Technology (CS & IT) 187
The adaptive summarization algorithm when applied over all the frames of the video gives the
set of frames which have high Visual attention index and do not contain redundant information,
which could be used in the Video Summary.
1 Input the video and get all the frames
2 Calculate Color histogram for each frame
3 Cluster the frames using K-Means clustering algorithm based on Euclidean distance.
4 Select Representative keyframes (frames having high VAI) from each cluster
5 Compare potential keyframes by calculating focus point shift between them.
Dij = | Σ (An
i
– An
j
)|
6 Extract 20 frames that have first 20 large Dij values.
7 Filter out redundant frames from the selected potential keyframes using the inequality
Dkey > Dave + δ Ddiv (δ = 1.5 here)
where Dave is the average VAI difference of the
keyframe, Ddiv is the standard deviation.
Thus by using the above algorithm, the most representative keyframes are extracted from the
pool of video frames.
5. EXPERIMENTAL RESULTS
The system proposed above is implemented using Matlab2011 and tested using random videos
downloaded from internet. The details of the videos used for testing the system and the no. of
keyframes extracted are given in Table 1. The Video summaries are obtained and are subjected
to user based evaluation first i.e. the videos along with the video summaries are presented to the
users. The summary of the videos produced are presented before 20 users, who are later made to
watch the video and their feedback on the effectiveness of the summary is obtained. The feedback
is presented in the Table 2.
The evaluation is based on three main criteria. Content Coverage refers to the extent to which
the summary effectively conveys the content of the video. Presentation indicates how effective
the presentation is. It refers to the entertainment quotient of the summary. Total effectiveness that
is rated out of 10 is based on total satisfaction provided by the summary.
Table 1. Video Details
Name of the
Video
URL Total
Number
of Frames
Total No. of
Keyframes
Extracted
Serj-Orders-
a-coffee
http://www.youtube.com/watch?v=u06IIqYY0iU 447 10
I’m normally
not a praying
Man
http://www.youtube.com/watch?v=MILArKLKUEk 219 5
Minecraft in a
nutshell
http://www.youtube.com/watch?v=7Dild9s2c94 135 7
Family-guy-
Im beautiful
http://www.youtube.com/watch?v=VTenT9PtM2w 120 7
Late-for-work http://www.youtube.com/watch?v=kfchvCyHmsc 193 10
Compliment
Taser
http://www.youtube.com/watch?v=q081Q8CZUnk 240 9
10. 188 Computer Science & Information Technology (CS & IT)
Table 2. User based Evaluation Results
Name of the video Content coverage Presentation Total Effectiveness
(Rated out of 10)
Serj-Orders-a-coffee Very Good Fair 8
I’m normally not a
praying Man
Good Fair 7
Minecraft in a nutshell Very Good Good 8
Family-guy-Im
beautiful
Good Good 8
Late-for-
work
Very Good Very good 8
Compliment Taser Good Good 7
The summaries produced by the system for the videos listed above are shown in the following
Figure 6
11. Computer Science & Information Technology (CS & IT) 189
Figure 6. Summaries of Test Videos
The Figure 6 shows summaries of the videos conveying the content of the video through the key
images obtained using the system explained. The user will be able to understand the content of
the video by looking at the summary without watching the video fully.
Precision and Recall of the video summaries produced by the system are presented in the Table 3
below. Precision is the fraction of right key frames of all keyframes extracted and recall is the
fraction of right key frames of all keyframes present. Here, the system is set in such a way that
each video has maximum representative 10 keyframes in the summary.
Table 3. Precision and Recall
Video
No.
Name of the Video Precision
1 Serj-Orders-a-coffee 0.778
2 I’m normally not a praying Man 0.6
3 Mine Craft in a nut shell 0.714
4 Family-guy Im not beautiful 0.856
5 Late-for-work 1
6 Compliment Taser 0.8
The Precision and Recall of all the videos are represented in the Figure 7 below:
0
0.2
0.4
0.6
0.8
1
video1 video3 video5
Precision
3-D Column 2
3-D Column 3
Figure 7. Precision Plot
12. 190 Computer Science & Information Technology (CS & IT)
Precision indicates how many keyframes are correct out of all the keyframes extracted. In video
1,two frames out of nine are redundant. So the precision is
5. CONCLUSION AND FUTURE ENHANCEMENTS
Thus the work undertaken helps us understanding that key frames extracted by the techniques that
take both color and motion information into account are more relevant than the ones that use
either color or motion information only to extract the keyframes. The effectiveness of the
keyframes extracted using both color and motion criteria is more when compared to that of the
ones extracted by other methods using either only color or only motion based techniques. Hence
to bring about a summarization which is in line with the high level human intuition both the color
conspicuousness and motion involved in a scene should be taken into account. From the user
response it is found that, our system produces a very effective summary. However in the system
proposed, the number of keyframes in the summary is independent of the duration and dynamicity
involved in the video. Summarization is efficient if the video is segmented using a suitable
method that takes both color and motion into account and then summarized by allocating
appropriate number of keyframes to each shot depending on the importance of the segment. So
the effectiveness can be further improved by determining the sufficient amount of keyframes
required to summarize the video based on the dynamicity of the video. Also the system can be
enhanced by allocating more keyframes representing shots that involve more action. This requires
an efficient shot boundary detection method. These are the enhancements that could be added to
the system in future. The system has been tested with web videos of very short duration(less than
one minute) that have no major change in background. However, long videos with multiple shots
and scenes will need appropriate shot segmentation step.
REFERENCES
[1] Zhonghua Sun, Kebin Jia ,Hexin Chen , “Video Key Frame Extraction Based on Spatial-temporal
Color Distribution”,International Conference on Intelligent information hiding and multimedia signal
processing, August 15-18,2008, Harbin, China.
[2] Nuno Vasconcelos ,Andrew Lippman, “A Spatiotemporal Motion Model for Video Summarization”,
IEEE computer society conference on computer vision and pattern recognition, pp. 361-366, 25-25
June, 1998
[3] Ajay Divakaran, Regunathan Radhakrishnan and Kadir A. Peker “Motion Activity-Based Extraction
Of Key-Frames From Video Shots”, International Conference on Image processing, Sep. 22-25,2002,
Volume 3, USA.
[4] Sathish kumar l.Varma and Sanjay N.Talbar, “Video summarization using dynamic threshold”,
International conference on Emerging trends in Engineering and Technology, Oct. 14-16, 2010.
[5] Jiang Pen And Qin Xiao Lin, “Key Frame Based Video Summary Using Visual Attention Clues” ,
IEEE Transactions On Multimedia 2010,pp. 64-73, Volume 17, Number 2, April.
[6] Michael Foster (1891). A Text-book of physiology. Lea Bros. & Co. p. 921.
[7] Aroh Barjatya, “Block Matching Algorithms for Motion Estimation” IEEE, DIP 6620 Spring, Paper
(2004) Final Project Paper, pp.1-6.
[8] P.Geetha and Vasumathi Narayanan, “A survey of Content based video Retrieval”, Journal of
Computer Science, pp. 474-486, Vol 4 (6), 2008.