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A comprehensive review of significant researches on content based indexing and retrieval of visual information

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Abstract

Developments in multimedia technologies have paved way for the storage of huge collections of video documents on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based analysis provides a flexible and powerfulway to access video data when compared with the other traditional video analysis techniques. The area of content based video indexing and retrieval (CBVIR), focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with enduring acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBVIR. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.

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References

  1. Hanis A, Sziranyi T. Measuring the motion similarity in video indexing. In: Proceedings of the 4th EURASIP Conference Focused on Video/Image Processing and Multimedia Communications. 2003, 507–512

    Google Scholar 

  2. Calic J, Izuierdo E. Efficient key-frame extraction and video analysis. In: Proceedings of the 2002 International Conference on Information Technology: Coding and Computing. 2002, 28–33

    Chapter  Google Scholar 

  3. Carbonaro A. Ontology-based video retrieval in a semantic-based learning environment. Journal of e-Learning and Knowledge Society, 2009, 4(3): 203–212

    MathSciNet  Google Scholar 

  4. George A, Rajakumar B, Suresh B. Markov random field based image restoration with aid of local and global features. International Journal of Computer Applications, 2012, 48(8): 23–28

    Article  Google Scholar 

  5. Kundra E H, Verma E M, Aashima E. Filter for removal of impulse noise by using fuzzy logic. International Journal of Image Processing (IJIP), 2011, 3(5): 195–202

    MATH  Google Scholar 

  6. Umamakeswari A, Rajaraman A. Object based video analysis, interpretation and tracking. Journal of Computer Science, 2007, 3(10): 818–822

    Article  Google Scholar 

  7. Amer A. Object-based video retrieval based on motion analysis and description. Technical Report, University du Québec, 1999

    Google Scholar 

  8. Javed O, Shah M, Comaniciu D. A probabilistic framework for object recognition in video. In: Proceedings of the 2004 International Conference on Image Processing. 2004, 2713–2716

    Google Scholar 

  9. Radhakrishnan R, Divakaran A, Xiong Z, Otsuka I. A content-adaptive analysis and representation framework for audio event discovery from unscripted multimedia. EURASIP Journal on Applied Signal Processing, 2006: 1–24

    Google Scholar 

  10. Schnettler B, Raab J. Interpretative visual analysis developments: state of the art and pending problems. Historical Social Research/Historische Sozialforschung, 2009, 265–295

    Google Scholar 

  11. Ramoser H, Schlogl T, Beleznai C, Winter M, Bischof H. Shape-based detection of humans for video surveillance applications. In: Proceedings of the 2003 IEEE International Conference on Image Processing. 2003, 3: 1013–1016

    Google Scholar 

  12. Ahmad A M, Lee S Y. Fast and robust object-extraction framework for object-based streaming system. International Journal of Virtual Technology and Multimedia, 2008, 1(1): 39–60

    Article  Google Scholar 

  13. Ngo C W, Pong T C, Zhang H J. Motion-based video representation for scene change detection. International Journal of Computer Vision, 2002, 50(2): 127–142

    Article  MATH  Google Scholar 

  14. Zelnik-Manor L, Irani M. Event-based analysis of video. In: Proceedings of the 2001 IEEE Conference on Computer Vision and Pattern Recognition. 2001, II-123–II-130

    Google Scholar 

  15. Ding Y, Fan G. Camera view-based american football video analysis. In: Proceedings of the 8th IEEE International Symposium on Multimedia. 2006, 317–322

    Chapter  Google Scholar 

  16. Mohan C K, Dhananjaya N, Yegnanarayana B. Video shot segmentation using late fusion technique. In: Proceedings of the 7th International Conference on Machine Learning and Applications. 2008, 267–270

    Google Scholar 

  17. Affendey L S, Mamat A, Ibrahim H, Ahmad F. Video data modelling to support hybrid query. International Journal of Computer Science and Network Security, 2007, 7(9): 53–61

    Google Scholar 

  18. Aytar Y, Shah M, Luo J. Utilizing semantic word similarity measures for video retrieval. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1–8

    Chapter  Google Scholar 

  19. Dimitrova N, Zhang H J, Shahraray B, Sezan I, Huang T, Zakhor A. Applications of video-content analysis and retrieval. IEEE MultiMedia, 2002, 9(3): 42–55

    Article  Google Scholar 

  20. Bergman L D, Castelli V, Li C. Progressive content-based retrieval from satellite image archives. Technical Report, D-Lib Magazine, 1997

    Google Scholar 

  21. Chang S F, Smith J R, Meng H J, Wang H, Zhong D. Finding images/video in large archives. Technical Report, D-Lib Magazine, 1997

    Google Scholar 

  22. Gupta A, Jain R. Visual information retrieval. Communications of the ACM, 1997, 40(5): 70–79

    Article  Google Scholar 

  23. Papadias D, Mantzourogiannis M, Kalnis P, Mamoulis N, Ahmad I. Content-based retrieval using heuristic search. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1999, 168–175

    Google Scholar 

  24. Koprinska I, Carrato S. Temporal video segmentation: a survey. Signal Processing: Image Communication, 2001, 16(5): 477–500

    Article  Google Scholar 

  25. Hanjalic A. Shot-boundary detection: unraveled and resolved? IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(2): 90–105

    Article  Google Scholar 

  26. Girgensohn A, Boreczky J. Time-constrained keyframe selection technique. In: Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems. 1999, 756–761

    Chapter  Google Scholar 

  27. Liu T, Kender J R. Optimization algorithms for the selection of key frame sequences of variable length. In: Proceedings of the 2002 European Conference on Computer Vision. 2002, 403–417

    Chapter  Google Scholar 

  28. Aslandogan Y A, Yu C T. Techniques and systems for image and video retrieval. IEEE Transactions on Knowledge and Data Engineering, 1999, 11(1): 56–63

    Article  Google Scholar 

  29. Lu G. Techniques and data structures for efficient multimedia retrieval based on similarity. IEEE Transactions on Multimedia, 2002, 4(3): 372–384

    Article  Google Scholar 

  30. Lelescu D, Schonfeld D. Video skimming and summarization based on principal component analysis. In: Proceedings of the 4th IFIP/IEEE International Conference on Management of Multimedia on the Internet. 2001, 128–141

    Chapter  Google Scholar 

  31. Gargi U, Kasturi R, Strayer S H. Performance characterization of video-shot-change detection methods. IEEE Transactions on Circuits and Systems for Video Technology, 2000, 10(1): 1–13

    Article  Google Scholar 

  32. Huang Y, Liu Q, Metaxas D. Video object segmentation by hypergraph cut. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1738–1745

    Chapter  Google Scholar 

  33. Aydm Alatan A, Tuncel E, Onural L. A rule-based method for object segmentation in video sequences. In: Proceedings of the 1997 International Conference on Image Processing. 1997, 522–525

    Google Scholar 

  34. Otoom A F, Gunes H, Piccardi M. Feature extraction techniques for abandoned object classification in video surveillance. In: Proceedings of the 15th IEEE International Conference on Image Processing. 2008, 1368–1371

    Google Scholar 

  35. Van Cauwelaert D. Generic models for adaptive robust feature extraction in video. In: Proceedings of the 9th FirW PhD Symposium. 2008, 148–149

    Google Scholar 

  36. Zhong D, Chang S F. An integrated approach for content-based video object segmentation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(8): 1259–1268

    Article  Google Scholar 

  37. Avrithis Y S, Doulamis A D, Doulamis N D, Kollias S D. An adaptive approach to video indexing and retrieval using fuzzy classification. In: Proceedings of VLBV. 1998

    Google Scholar 

  38. Idris F, Panchanathan S. Review of image and video indexing techniques. Journal of Visual Communication and Image Representation, 1997, 8(2): 146–166

    Article  Google Scholar 

  39. Christel M G, Smith M A, Taylor C R, Winkler D B. Evolving video skims into useful multimedia abstractions. In: Proceedings of the 1998 SIGCHI Conference on Human factors in Computing Systems. 1998, 171–178

    Google Scholar 

  40. Marchand-Maillet S. Content-based video retrieval: an overview. 2000

    Google Scholar 

  41. Jawahar C, Chennupati B, Paluri B, Jammalamadaka N. Video Retrieval Based on Textual Queries. Technical Report, 2005

    Google Scholar 

  42. Zhang X P, Chen Z. An automated video object extraction system based on spatiotemporal independent component analysis and multiscale segmentation. EURASIP Journal on Applied Signal Processing, 2006, 2006: 184

    Google Scholar 

  43. Chang Y, Lee D J, Hong Y, Archibald J. Unsupervised video shot detection using clustering ensemble with a color global scale-invariant feature transform descriptor. Journal on Image and Video Processing, 2008, 9

    Google Scholar 

  44. Kolekar M H, Palaniappan K, Sengupta S, Seetharaman G. Semantic concept mining based on hierarchical event detection for soccer video indexing. Journal of Multimedia, 2009, 4(5): 298–312

    Article  Google Scholar 

  45. Jiang R, Crookes D. Approach to automatic video motion segmentation. Electronics Letters, 2007, 43(18): 968–970

    Article  Google Scholar 

  46. Basharat A, Zhai Y, Shah M. Content based video matching using spatiotemporal volumes. Computer Vision and Image Understanding, 2008, 110(3): 360–377

    Article  Google Scholar 

  47. Kuo T C, Chen A L. A maskmatching approach for video segmentation on compressed data. Information Sciences, 2002, 141(1): 169–191

    Article  MathSciNet  MATH  Google Scholar 

  48. Chen D Y, Hsiao MH, Lee S Y. Automatic closed caption detection and filtering in mpeg videos for video structuring. Journal of Information Science and Engineering, 2006, 22(5): 1145–1162

    Google Scholar 

  49. Duan L Y, Xu M, Tian Q, Xu C S, Jin J S. A unified framework for semantic shot classification in sports video. IEEE Transactions on Multimedia, 2005, 7(6): 1066–1083

    Article  Google Scholar 

  50. Liu S, Xu M, Yi H, Chia L T, Rajan D. Multimodal semantic analysis and annotation for basketball video. EURASIP Journal on Applied Signal Processing, 2006: 182

    Google Scholar 

  51. Han B, Gao X, Ji H. A shot boundary detection method for news video based on rough-fuzzy sets. International Journal of Information Technology, 2005, 11(7): 101–111

    Google Scholar 

  52. Gao X, Tang X. Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(9): 765–776

    Article  Google Scholar 

  53. Zhai Y, Shah M. Video scene segmentation using markov chain monte carlo. IEEE Transactions on Multimedia, 2006, 8(4): 686–697

    Article  Google Scholar 

  54. Fan J, Aref W G, Elmagarmid A K, Hacid M S, Marzouk M S, Zhu X. Multiview: multilevel video content representation and retrieval. Journal of Electronic Imaging, 2001, 10(4): 895–908

    Article  Google Scholar 

  55. Dönderler M E, Ulusoy Ö, Güdükbay U. Rule-based spatiotemporal query processing for video databases. The VLDB Journal, 2004, 13(1): 86–103

    Article  Google Scholar 

  56. Erozel G, Cicekli N K, Cicekli I. Natural language querying for video databases. Information Sciences, 2008, 178(12): 2534–2552

    Article  Google Scholar 

  57. Smeaton A F, Wilkins P, Worring M, De Rooij O, Chua T S, Luan H. Content-based video retrieval: three example systems from trecvid. International Journal of Imaging Systems and Technology, 2008, 18(2–3): 195–201

    Article  Google Scholar 

  58. Schonfeld D, Lelescu D. Vortex: video retrieval and tracking from compressed multimedia databases-multiple object tracking from mpeg-2 bit stream. Journal of Visual Communication and Image Representation, 2000, 11(2): 154–182

    Article  Google Scholar 

  59. Vrochidis S, Doulaverakis C, Gounaris A, Nidelkou E, Makris L, Kompatsiaris I. A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections. International Journal of Metadata, Semantics and Ontologies (IJMSO), 2008, 3(3): 167–182

    Article  Google Scholar 

  60. Wen C Y, Chang L F, Li H H. Content based video retrieval with motion vectors and the rgb color model. Forensic Science Journal, 2007, 6(2): 1–36

    Google Scholar 

  61. Lili N, Noah S, Khalid F. Extracting and integrating multimodality features via multidimensional approach for video retrieval. International Journal of Computer Science and Network Security, 2009, 9(2): 252

    Google Scholar 

  62. Hoi S C, Lyu M R. A multimodal and multilevel ranking scheme for large-scale video retrieval. IEEE Transactions on Multimedia, 2008, 10(4): 607–619

    Article  Google Scholar 

  63. Tjondronegoro D, Chen Y P P. Content-based indexing and retrieval using mpeg-7 and x-query in video data management systems. World Wide Web, 2002, 5(3): 207–227

    Article  Google Scholar 

  64. Ma Y F, Zhang H J. Motion pattern-based video classification and retrieval. EURASIP Journal on Applied Signal Processing, 2003: 199–208

    Google Scholar 

  65. DeMenthon D, Doermann D. Video retrieval of near-duplicates using κ-nearest neighbor retrieval of spatio-temporal descriptors. Multimedia Tools and Applications, 2006, 30(3): 229–253

    Article  Google Scholar 

  66. Yang J, Li Q, Wenyin L, Zhuang Y. Searching for flash movies on the web: a content and context based framework. World Wide Web, 2005, 8(4): 495–517

    Article  Google Scholar 

  67. Dagtas S, Al-Khatib W, Ghafoor A, Kashyap R L. Models for motionbased video indexing and retrieval. IEEE Transactions on Image Processing, 2000, 9(1): 88–101

    Article  Google Scholar 

  68. Lu H, Ooi B C, Shen H T, Xue X. Hierarchical indexing structure for efficient similarity search in video retrieval. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(11): 1544–1559

    Article  Google Scholar 

  69. Zhang D, Nunamaker J F. A natural language approach to contentbased video indexing and retrieval for interactive e-learning. IEEE Transactions on Multimedia, 2004, 6(3): 450–458

    Article  Google Scholar 

  70. Amir A, Srinivasan S, Efrat A. Search the audio, browse the video- a generic paradigm for video collections. EURASIP Journal on Advances in Signal Processing, 1900, 2003(2): 209–222

    Article  Google Scholar 

  71. Chiu C Y, Chao S P, Wu M Y, Yang S N, Lin H C. Content-based retrieval for human motion data. Journal of Visual Communication and Image Representation, 2004, 15(3): 446–466

    Article  Google Scholar 

  72. Munesawang P, Guan L. Adaptive video indexing and automatic/semiautomatic relevance feedback. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15(8): 1032–1046

    Article  Google Scholar 

  73. Fablet R, Bouthemy P, Pérez P. Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval. IEEE Transactions on Image Processing, 2002, 11(4): 393–407

    Article  Google Scholar 

  74. Yi H, Rajan D, Chia L T. A new motion histogram to index motion content in video segments. Pattern Recognition Letters, 2005, 26(9): 1221–1231

    Article  Google Scholar 

  75. Babu R V, Ramakrishnan K. Compressed domain video retrieval using object and global motion descriptors. Multimedia Tools and Applications, 2007, 32(1): 93–113

    Article  Google Scholar 

  76. Snoek C G, Worring M, Koelma D C, Smeulders A W. A learned lexicon-driven paradigm for interactive video retrieval. IEEE Transactions on Multimedia, 2007, 9(2): 280–292

    Article  Google Scholar 

  77. Abdelali A B, Mtibaa A, Bourennane E, Abid M. Design of hardware accelerators for content based video indexing. Asian Journal of Information Technology, 2006, 5(9): 976–984

    Google Scholar 

  78. Doulamis A D, Doulamis N D, Kollias S D. A fuzzy video content representation for video summarization and content-based retrieval. Signal Processing, 2000, 80(6): 1049–1067

    Article  MATH  Google Scholar 

  79. Lee J, Dickinson B W. Hierarchical video indexing and retrieval for subband-coded video. IEEE Transactions on Circuits and Systems for Video Technology, 2000, 10(5): 824–829

    Article  Google Scholar 

  80. Fan J, Zhu X, Hacid M S, Elmagarmid A K. Model-based video classification toward hierarchical representation, indexing and access. Multimedia Tools and Applications, 2002, 17(1): 97–120

    Article  Google Scholar 

  81. Albanese M, Chianese A, Moscato V, Sansone L. A formal model for video shot segmentation and its application via animate vision. Multimedia Tools and Applications, 2004, 24(3): 253–272

    Article  Google Scholar 

  82. Cheung R. Indexing an intelligent video database using evolutionary control. Journal of Digital Information Management, 2003, 1: 8–19

    Google Scholar 

  83. Erol B, Kossentini F. Retrieval by local motion. EURASIP Journal on Advances in Signal Processing, 1900, 2003(1): 41–47

    Article  Google Scholar 

  84. Xu C, Cheng J, Zhang Y, Zhang Y, Lu H. Sports video analysis: semantics extraction, editorial content creation and adaptation. Journal of Multimedia, 2009, 4(2): 69–79

    Article  Google Scholar 

  85. Lee S, Yoo C D. Robust video fingerprinting for content-based video identification. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(7): 983–988

    Article  Google Scholar 

  86. Fan J, Elmagarmid A K, Zhu X, Aref W G, Wu L. Classview: hierarchical video shot classification, indexing, and accessing. IEEE Transactions on Multimedia, 2004, 6(1): 70–86

    Article  Google Scholar 

  87. Fan J, Luo H, Elmagarmid A K. Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing. IEEE Transactions on Image Processing, 2004, 13(7): 974–992

    Article  Google Scholar 

  88. Khan A, Sun L, Ifeachor E. Content-based video quality prediction for mpeg4 video streaming over wireless networks. Journal ofMultimedia, 2009, 4(4): 228–239

    Google Scholar 

  89. Dumont E, Merialdo B. Rushes video summarization and evaluation. Multimedia Tools and Applications, 2010, 48(1): 51–68

    Article  Google Scholar 

  90. Thyagharajan K, Ramachandran V. An effective transmission and browsing methodology for streaming video. Journal of Computer Science, 2006, 2(4): 326–332

    Article  Google Scholar 

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Correspondence to R. Priya.

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R. PRIYA received BS in Computer Science from Madras University and MS in Software Engineering from Annamalai University, in 1998 and 2001, respectively. She is currently a research scholar in Department of Mathematics, Anna University-Chennai, India. Her research interests are data mining, content based image and video retrieval.

Dr. T. N. Shanmugam is currently a professor in Department of Mathematics, Anna University, and Chennai, India. He received his BS in Mathematics from The New College, Chennai and MS from Ramanujan Institute for Advanced study Mathematics, University of Madras. He received his PhD from Anna University-Chennai in the year 1990. He has been a co-ordinator of the Video Technology Lab of Anna University-Chennai. His research interests are in complex function theory and multimedia streaming, with about eighty research papers in international journals and conferences.

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Priya, R., Shanmugam, T.N. A comprehensive review of significant researches on content based indexing and retrieval of visual information. Front. Comput. Sci. 7, 782–799 (2013). https://doi.org/10.1007/s11704-013-1276-6

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