To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Microcontroller system is one of the vital subjects offered by students during the sequence of study in universities and other colleges of science, engineering and technology in the world. In this paper, we solve the problem of student comprehension and skill development in embedded system design using microcontroller chip PIC16F887 by demonstration of hands-on laboratory experiments. Also, developments of software code, circuit diagram simulation were carried out. This is to help students connect their theoretical knowledge with the practical experience. Each of the experiments was carried out using BK300 development board, PICKit3 programmer, Proteus 8.0 software. Our years of experience in the teaching of microcontroller course and the active involvement of students as manifested in complete in-depth hands-on laboratory projects on real life problem solving. Laboratory session with the development board and software demonstrated in this article is unambiguous. Future embedded system laboratory session could be designed around ATMel lines of Microcontrollers.
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.
Exploring visual and motion saliency for automatic video object extractionMuthu Samy
Sybian Technologies Pvt Ltd
Final Year Projects & Real Time live Projects
JAVA(All Domains)
DOTNET(All Domains)
ANDROID
EMBEDDED
VLSI
MATLAB
Project Support
Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
Supporting Documents, Software E-Books,
Software Development Standards & Procedure
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24/7 lab session
Final Year Projects For BE,ME,B.Sc,M.Sc,B.Tech,BCA,MCA
PROJECT DOMAIN:
Cloud Computing
Networking
Network Security
PARALLEL AND DISTRIBUTED SYSTEM
Data Mining
Mobile Computing
Service Computing
Software Engineering
Image Processing
Bio Medical / Medical Imaging
Contact Details:
Sybian Technologies Pvt Ltd,
No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
Chennai-600 017
Ph:044 42070551
Mobile No:9790877889,9003254624,7708845605
Mail Id:sybianprojects@gmail.com,sunbeamvijay@yahoo.com
Unsupervised object-level video summarization with online motion auto-encoderNEERAJ BAGHEL
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day.
Author investigate a pioneer research direction towards the unsupervised object-level video summarization.
It can be distinguished from existing pipelines in two aspects:
Extracting key motions of participated objects
Learning to summarize in an unsupervised and online manner.
Microcontroller system is one of the vital subjects offered by students during the sequence of study in universities and other colleges of science, engineering and technology in the world. In this paper, we solve the problem of student comprehension and skill development in embedded system design using microcontroller chip PIC16F887 by demonstration of hands-on laboratory experiments. Also, developments of software code, circuit diagram simulation were carried out. This is to help students connect their theoretical knowledge with the practical experience. Each of the experiments was carried out using BK300 development board, PICKit3 programmer, Proteus 8.0 software. Our years of experience in the teaching of microcontroller course and the active involvement of students as manifested in complete in-depth hands-on laboratory projects on real life problem solving. Laboratory session with the development board and software demonstrated in this article is unambiguous. Future embedded system laboratory session could be designed around ATMel lines of Microcontrollers.
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.
Exploring visual and motion saliency for automatic video object extractionMuthu Samy
Sybian Technologies Pvt Ltd
Final Year Projects & Real Time live Projects
JAVA(All Domains)
DOTNET(All Domains)
ANDROID
EMBEDDED
VLSI
MATLAB
Project Support
Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
Supporting Documents, Software E-Books,
Software Development Standards & Procedure
E-Book, Theory Classes, Lab Working Programs, Project Design & Implementation
24/7 lab session
Final Year Projects For BE,ME,B.Sc,M.Sc,B.Tech,BCA,MCA
PROJECT DOMAIN:
Cloud Computing
Networking
Network Security
PARALLEL AND DISTRIBUTED SYSTEM
Data Mining
Mobile Computing
Service Computing
Software Engineering
Image Processing
Bio Medical / Medical Imaging
Contact Details:
Sybian Technologies Pvt Ltd,
No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
Chennai-600 017
Ph:044 42070551
Mobile No:9790877889,9003254624,7708845605
Mail Id:sybianprojects@gmail.com,sunbeamvijay@yahoo.com
Unsupervised object-level video summarization with online motion auto-encoderNEERAJ BAGHEL
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day.
Author investigate a pioneer research direction towards the unsupervised object-level video summarization.
It can be distinguished from existing pipelines in two aspects:
Extracting key motions of participated objects
Learning to summarize in an unsupervised and online manner.
Automatic semantic content extraction in videos using a fuzzy ontology and ru...ecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
System analysis and design for multimedia retrieval systemsijma
Due to the extensive use of information technology and the recent developments in multimedia systems, the
amount of multimedia data available to users has increased exponentially. Video is an example of
multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio.
Content based video retrieval is an approach for facilitating the searching and browsing of large
multimedia collections over WWW. In order to create an effective video retrieval system, visual perception
must be taken into account. We conjectured that a technique which employs multiple features for indexing
and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate
this, content based indexing and retrieval systems were implemented using color histogram, Texture feature
(GLCM), edge density and motion..
The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
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.
Profile based Video segmentation system to support E-learningGihan Wikramanayake
S C Premaratne, D D Karunaratna, G N Wikramanayake, K P Hewagamage, G K A Dias (2004) "Profile Based Video Segmentation System to Support e-Learning" In:6th International Information Technology Conference, Edited by:V.K. Samaranayake et al. pp. 74-81. Infotel Lanka Society, Colombo, Sri Lanka: IITC Nov 29-Dec 1, ISBN: 955-8974-01-3
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.
Video Data Visualization System : Semantic Classification and Personalization ijcga
We present in this paper an intelligent video data visualization tool, based on semantic classification, for
retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification
resulting from semantic analysis of video. The obtained classes will be projected in the visualization space.
The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the
edges are the relation between documents and the classes of documents. Finally, we construct the user’s
profile, based on the interaction with the system, to render the system more adequate to its preferences.
Video Data Visualization System : Semantic Classification and Personalization ijcga
We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the
edges are the relation between documents and the classes of documents. Finally, we construct the user’s profile, based on the interaction with the system, to render the system more adequate to its preferences.
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
Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
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Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
System analysis and design for multimedia retrieval systemsijma
Due to the extensive use of information technology and the recent developments in multimedia systems, the
amount of multimedia data available to users has increased exponentially. Video is an example of
multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio.
Content based video retrieval is an approach for facilitating the searching and browsing of large
multimedia collections over WWW. In order to create an effective video retrieval system, visual perception
must be taken into account. We conjectured that a technique which employs multiple features for indexing
and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate
this, content based indexing and retrieval systems were implemented using color histogram, Texture feature
(GLCM), edge density and motion..
The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
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.
Profile based Video segmentation system to support E-learningGihan Wikramanayake
S C Premaratne, D D Karunaratna, G N Wikramanayake, K P Hewagamage, G K A Dias (2004) "Profile Based Video Segmentation System to Support e-Learning" In:6th International Information Technology Conference, Edited by:V.K. Samaranayake et al. pp. 74-81. Infotel Lanka Society, Colombo, Sri Lanka: IITC Nov 29-Dec 1, ISBN: 955-8974-01-3
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.
Video Data Visualization System : Semantic Classification and Personalization ijcga
We present in this paper an intelligent video data visualization tool, based on semantic classification, for
retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification
resulting from semantic analysis of video. The obtained classes will be projected in the visualization space.
The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the
edges are the relation between documents and the classes of documents. Finally, we construct the user’s
profile, based on the interaction with the system, to render the system more adequate to its preferences.
Video Data Visualization System : Semantic Classification and Personalization ijcga
We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the
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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
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Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
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Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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Automatic semantic content extraction in videos using a fuzzy ontology and rule based model
1. Automatic Semantic Content Extraction in Videos Using a Fuzzy
Ontology and Rule-Based Model
Abstract
Recent increase in the use of video-based applications has revealed the need for extracting the content in
videos. Raw data and low-level features alone are not sufficient to fulfill the user ’s needs; that is, a deeper
understanding of the content at the semantic level is required. Currently, manual techniques, which are
inefficient, subjective and costly in time and limit the querying capabilities, are being used to bridge the gap
between low-level representative features and high-level semantic content. Here, we propose a semantic content
extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted
automatically. We introduce an ontology-based fuzzy video semantic content model that uses spatial/temporal
relations in event and concept definitions. This metaontology definition provides a wide-domain applicable rule
construction standard that allows the user to construct an ontology for a given domain. In addition to domain
ontologies, we use additional rule definitions (without using ontology) to lower spatial relation computation
cost and to be able to define some complex situations more effectively. The proposed framework has been fully
implemented and tested on three different domains. We have obtained satisfactory precision and recall rates for
object, event and concept extraction.
Existing System
The rapid increase in the available amount of video data has caused an urgent need to develop intelligent
methods to model and extract the video content. Typical applications in which modeling and extracting video
content are crucial include surveillance, video-on-demand systems, intrusion detection, border monitoring,
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2. sport events, criminal investigation systems, and many others. the ultimate goal is to enable users to retrieve
some desired content from massive amounts of video data in an efficient and semantically meaningful manner.
there are basically three levels of video content which are raw video data, low-level features and semantic
content. first, raw video data consist of elementary physical video units together with some general video
attributes such as format, length, and frame rate. second, low-level features are characterized by audio, text, and
visual features such as texture, color distribution, shape, motion, etc. third, semantic content contains high-level
concepts such as objects and events. the first two levels on which content modeling and extraction approaches
are based use automatically extracted data, which represent the low-level content of a video, but they hardly
provide semantics which is much more appropriate for users.
Disadvantages
Therefore, raw video data and low-level features alone are not sufficient to fulfill the
user’s need; that is, a deeper understanding of the information at the semantic level is
required in many video-based applications.
it is very difficult to extract semantic content directly from raw video data. This is
because video is a temporal sequence of frames without a direct relation to its semantic
content
Proposed System
This study proposes an automatic semantic content extraction framework. This is accomplished through
the development of an ontology-based semantic content model and semantic content extraction algorithms. Our
work differs from other semantic content extraction and representation studies in many ways and contributes to
semantic video modeling and semantic content extraction research areas. First of all, we propose a
metaontology, a rule construction standard which is domain independent, to construct domain
ontologies.Besides, generic ontology models provide solutions for multimedia structure representations. In this
study, we propose a wide-domain applicable video content model in order to model the semantic content in
videos. VISCOM is a well-defined metaontology for constructing domain ontologies. It is an alternative to the
rule-based and domain-dependent extraction methods. Constructing rules for extraction is a tedious task and is
not scalable. Without any standard on rule construction, different domains can have different rules with
different syntax. In addition to the complexity of handling such difference, each rule structure can have
weaknesses. Besides, VISCOM provides a standardized rule construction ability with the help of its
metaontology. It eases the rule construction process and makes its use on larger video data possible.
3. Advantages
Ontology provides many advantages and capabilities for content modeling. Yet, a great majority of the
ontologybased video content modeling studies propose domain specific ontology models limiting its use
to a specific domain.
the ontology model and the semantic content extraction process is developed considering
uncertainty issues.
Module
1. Input Video
2. Feature Extraction
3. Domain Ontology
4. Relation Extraction
5. Spatial Relation
6. VISCOM (Video Semantic Content Model )
Module Description
1. Input Video
The browser the user in the entire video database stored in the search the get input video.
2. Feature Extraction
The play video extract different several of the frame extraction the image in store the temper
very stores the image. The Automatic Semantic Content Extraction Framework is illustrated. The
ultimate goal of ASCEF is to extract all of the semantic content existing in video instances. In order to
achieve this goal, the automatic semantic content extraction framework.
3. Domain Ontology
The linguistic part of VISCOM contains classes and relations between these classes. Some of the classes
represent semantic content types such as Object and Event while others are used in the automatic semantic
content extraction process. Relations defined in VISCOM give ability to model events and concepts related
with other objects and events. VISCOM is developed on an ontology-based structure where semantic
4. content types and relations between these types are collected under VISCOM Classes, VISCOM Data
Properties which associate classes with constants and VISCOM Object Properties which are used to define
relations between classes. In addition, there are some domain independent class individuals.
4. Relation Extraction
The framework, temporal relations are utilized in order to add temporality to sequence Spatial
Change or Events individuals in the definition of Event individuals. the well-known formalisms proposed
for temporal reasoning is Allen’s temporal interval algebra which describes a temporal representation that
takes the notion of a temporal interval as primitive.
5. Spatial Relation
The spatial relation instances having these spatial relation types are extracted by using the rule
definitions. Initially, the spatial relation computation time is calculated for the case where no rule definition is
made. Then, the rules are defined one by one and the computation times are calculated after adding each rule
definition. As it can be seen the spatial relation computation times are decreased with the increase in the number
of rules definitions.
6. VISCOM
That the proposed ontology-based automatic semantic content extraction framework is successful for both
event and concept extraction. There are two points that must be ensured to achieve this success. The first one is
to obtain object instances correctly. Whenever a missing or misclassified object instance occurs in the object
instance set, which is used by the framework as input, success of event and concept extraction decreases. The
second issue is to use the proposed VISCOM met model effectively and construct well and correctly defined
domain ontology. Wrong, extra, or missing definitions in the constructed ontology can decrease the extraction
success. In the tests, we have encountered wrong extractions because of the wrong Similarity class individual
definitions for typing event in office domain.
6. Conclusion
The primary aim of this research is to develop a framework for an automatic semantic content extraction system
for videos which can be utilized in various areas, such as surveillance, sport events, and news video
applications. The novel idea here is to utilize domain ontologism generated with a domain-independent
ontology-based semantic content met ontology model and a set of special rule definitions. Automatic Semantic
Content Extraction Framework contributes in several ways to semantic video modeling and semantic content
extraction research areas. First of all, the semantic content extraction process is done automatically. In addition,
a generic ontology-based semantic met ontology model for videos (VISCOM) is proposed. Moreover, the
semantic content representation capability and extraction success are improved by adding fuzziness in class,
relation, and rule definitions. An automatic Genetic Algorithm-based object extraction method is integrated to
the proposed system to capture semantic content. In every component of the framework, ontology-based
modeling and extraction capabilities are used. The test results clearly show the success of the developed system.
As a further study, one can improve the model and the extraction capabilities of the framework for spatial
relation extraction by considering the viewing angle of camera and the motions in the depth dimension.
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