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dc.contributor.author
Tsafaris, Konstantinos
en
dc.date.accessioned
2015-05-29T19:18:02Z
dc.date.available
2015-09-27T05:56:28Z
dc.date.issued
2015-05-29
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/124
dc.rights
Default License
dc.title
Interactive video search based on online content classification
en
heal.type
masterThesis
heal.language
en
heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
heal.publicationDate
2014-11
heal.bibliographicCitation
Tsafaris Konstantinos , 2014, Interactive video search based on online content classification,Master's Dissertation, International Hellenic University
en
heal.abstract
Technology evolves in amazingly rapid speeds. Device capabilities and capacities are increasing, while costs are going down. This leads to a great increase in the number of multimedia content available worldwide. Video is a very interesting but yet complex multimedia component, and in order to quickly and efficiently access it, advanced video search engines have to be developed. In this thesis, we focus on expanding the capabilities of the VERGE video search engine, by designing and developing a new module that is based on online content classification. Its main advantage compared to other modules usually found in similar video search engines, is that it uses supervised machine learning methods with an automatically created dataset and exploits existing web search engines. The training set is gathered ‘on the fly’, which means that there is no limitation to the search query keywords. Visual features are extracted from the images of both the training and the testing data set, which are used as the input to a supervised machine learning classifier. The classifier’s purpose is to separate relevant from irrelevant videos, and return the best matches back to the user. In order to evaluate the performance of the online content classification module, experiments for the retrieval of videos based on various search queries were conducted. For each search query, several combinations of training set options and visual descriptors were used. The results are rather promising, and show that the online content classification system might become a useful addition to any multimedia search engine.
en
heal.tableOfContents
ACKNOWLEDGEMENTS ................................................................................................................. V ABSTRACT ...................................................................................................................... VII ABBREVIATIONS ....................................................................................................................... IX 1 INTRODUCTION ..................................................................................................................... 1 2 LITERATURE REVIEW ............................................................................................................. 5 2.1 Video definition ................................................................................................................ 5 2.2 Video indexing and retrieval ............................................................................................. 7 2.3 Video indexing .................................................................................................................. 8 2.3.1 Shot segmentation ................................................................................................ 10 2.3.2 Key frame extraction ............................................................................................. 12 2.3.3 Visual features ....................................................................................................... 13 2.3.4 Textual features ..................................................................................................... 17 2.4 Video retrieval ................................................................................................................18 2.4.1 Query by text ......................................................................................................... 19 2.4.2 Query by visual example ....................................................................................... 20 2.4.3 Query by concept .................................................................................................. 21 2.4.4 Relevance feedback ............................................................................................... 21 2.4.5 Query expansion ................................................................................................... 22 2.5 Interfaces for video retrieval ..........................................................................................24 2.6 Evaluation benchmarks and events ................................................................................24 3 INTERACTIVE VIDEO SEARCH ENGINE .................................................................................29 3.1 Search engine framework ...............................................................................................29 3.2 Technologies ...................................................................................................................30 3.3 Modules ..........................................................................................................................31 4 ONLINE CONTENT CLASSIFICATION .....................................................................................34 4.1 Search Engine API ...........................................................................................................35 4.2 JSON Decoder .................................................................................................................36 xi 4.3 Downloader .................................................................................................................... 38 4.4 Feature Extraction .......................................................................................................... 38 4.4.1 MPEG-7 .................................................................................................................. 39 4.4.2 SIFT/SURF ............................................................................................................... 42 4.5 Normalization ................................................................................................................. 43 4.5.1 MPEG-7 .................................................................................................................. 43 4.5.2 SIFT/SURF ............................................................................................................... 44 4.6 Support Vector Machine Classifier ................................................................................. 44 4.6.1 SVM Training .......................................................................................................... 45 4.6.2 SVM Testing ........................................................................................................... 49 5 RESULTS ...................................................................................................................... 50 5.1 Overview ........................................................................................................................ 50 5.2 Test data set ................................................................................................................... 51 5.3 Query .............................................................................................................................. 51 5.4 Visual descriptors – MPEG7 ........................................................................................... 52 5.5 Visual descriptors – SIFT/SURF ....................................................................................... 57 5.6 Efficiency of the Search Engine API ................................................................................ 57 5.7 Speed of the Search Engine API ..................................................................................... 61 6 CONCLUSIONS ..................................................................................................................... 69 7 BIBLIOGRAPHY .................................................................................................................... 71 A. APPENDIX ...................................................................................................................... 79
en
heal.advisorName
Bassiliadis, Prof. Nick
en
heal.committeeMemberName
Bassiliadis, Prof. Nick
en
heal.committeeMemberName
Kompatsiaris, Dr. Ioannis
en
heal.committeeMemberName
Berberidis, C.
en
heal.academicPublisher
School of Science &Technology, Master of Science (MSc) in Information and Communication Systems
en
heal.academicPublisherID
ihu
heal.numberOfPages
101
heal.fullTextAvailability
true


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