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Image Feature Detectors and Descriptors pp 173–202Cite as

Redundancy Elimination in Video Summarization

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 630))

Abstract

Video summarization is a task which aims at presenting the contents of a video to the user in a succinct manner so as to reduce the retrieval and browsing time. At the same time sufficient coverage of the contents is to be ensured. A trade-off between conciseness and coverage has to be reached as these properties are conflicting to each other. Various feature descriptors have been developed which can be used for redundancy removal in the spatial and temporal domains. This chapter takes an insight into the various strategies for redundancy removal. A method for intra-shot and inter-shot redundancy removal for static video summarization is also presented. High values of precision and recall illustrate the efficacy of the proposed method on a dataset consisting of videos with varied characteristics.

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References

  1. Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content-based video retrieval and browsing. Pattern Recogn. 30(4), 643–658 (1997)

    Article  Google Scholar 

  2. Chang, S.F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: A fully automated content-based video search engine supporting spatiotemporal queries. IEEE Trans. Circuits Syst. Video Technol. 8(5), 602–615 (1998)

    Article  Google Scholar 

  3. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 2(1), 1–19 (2006)

    Google Scholar 

  4. Papadimitriou, C.H., Tamaki, H., Raghavan, P., Vempala, S.: Latent semantic indexing: a probabilistic analysis. In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 159–168. ACM (1998)

    Google Scholar 

  5. Kim, H.S., Lee, J., Liu, H., Lee, D.: Video linkage: group based copied video detection. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 397–406. ACM (2008)

    Google Scholar 

  6. Kim, C., Hwang, J.N.: Object-based video abstraction for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1128–1138 (2002)

    Article  Google Scholar 

  7. Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Trans. Image Proc. 12(7), 796–807 (2003)

    Article  Google Scholar 

  8. Babaguchi, N., Kawai, Y., Ogura, T., Kitahashi, T..: Personalized abstraction of broadcasted American football video by highlight selection. IEEE Trans. Multimedia 6(4), 575–586 (2004)

    Google Scholar 

  9. Pan, H., Van Beek, P., Sezan, M.I.: Detection of slow-motion replay segments in sports video for highlights generation. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1649–1652 (2001)

    Google Scholar 

  10. Tjondronegoro, D.W., Chen, Y.P.P., Pham, B.: Classification of self-consumable highlights for soccer video summaries. In: 2004 IEEE International Conference on Multimedia and Expo, 2004. ICME’04, vol. 1, pp. 579–582. IEEE (2004)

    Google Scholar 

  11. Nam, J., Tewfik, A.H.: Dynamic video summarization and visualization. In: Proceedings of the Seventh ACM International Conference on Multimedia (Part 2), pp. 53–56. ACM (1999)

    Google Scholar 

  12. Pfeiffer, S., Lienhart, R., Fischer, S., Effelsberg, W.: Abstracting digital movies automatically. J. Vis. Commun. Image Represent. 7(4), 345–353 (1996)

    Google Scholar 

  13. Yeung, M.M., Yeo, B.L.: Video visualization for compact presentation and fast browsing of pictorial content. IEEE Trans. Circuits Syst. Video Technol. 7(5), 771–785 (1997)

    Google Scholar 

  14. Moriyama, T., Sakauchi, M.: Video summarisation based on the psychological content in the track structure. In: Proceedings of the 2000 ACM Workshops on Multimedia, pp. 191–194. ACM (2000)

    Google Scholar 

  15. Yeung, M.M, Yeo, B.L.: Video content characterization and compaction for digital library applications. In: Electronic Imaging’97, pp. 45–58 (1997)

    Google Scholar 

  16. Lienbart, R., Pfeiffer, S., Effelsberg, W.: Scene determination based on video and audio features. In: IEEE International Conference on Multimedia Computing and Systems, 1999, vol. 1, pp. 685–690. IEEE (1999)

    Google Scholar 

  17. Thakore, V.H.: Video shot cut boundary detection using histogram. Int. J. Eng. Sci. Res. Technol. (IJESRT) 2, 872–875 (2013)

    Google Scholar 

  18. Baber, J., Afzulpurkar, N., Dailey, M.N., Bakhtyar, M.: Shot boundary detection from videos using entropy and local descriptor. In: 2011 17th International Conference on Digital Signal Processing (DSP), pp. 1–6. IEEE (2011)

    Google Scholar 

  19. Cernekova, Z., Nikou, C., Pitas, I.: Shot detection in video sequences using entropy based metrics. In: 2002 International Conference on Image Processing. 2002. Proceedings, vol. 3, p. III-421. IEEE (2002)

    Google Scholar 

  20. Hampapur, A., Jain, R., Weymouth, T.E.: Production model based digital video segmentation. Multimedia Tools Appl. 1(1), 9–46 (1995)

    Article  Google Scholar 

  21. Zhang, H., Kankanhalli, A., Smoliar, S.W.: Automatic partitioning of full-motion video. Multimedia Syst. 1(1), 10–28 (1993)

    Article  Google Scholar 

  22. Tonomura, Y.: Video handling based on structured information for hypermedia systems. In: International conference on Multimedia Information Systems’ 91, pp. 333–344. McGraw-Hill Inc. (1991)

    Google Scholar 

  23. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)

    Article  Google Scholar 

  24. Wang, T., Wu, Y., Chen, L.: An approach to video key-frame extraction based on rough set. In: International Conference on Multimedia and Ubiquitous Engineering, 2007. MUE’07, pp. 590–596. IEEE (2007)

    Google Scholar 

  25. Li, B., Sezan, M.I.: Event detection and summarization in sports video. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, 2001. (CBAIVL 2001), pp. 132–138. IEEE (2001)

    Google Scholar 

  26. Potapov, D., Douze, M., Harchaoui, Z., Schmid, C.: Category-specific video summarization. In: Computer Vision-ECCV 2014, pp. 540–555. Springer (2014)

    Google Scholar 

  27. Wang, F., Ngo, C.W.: Rushes video summarization by object and event understanding. In: Proceedings of the International Workshop on TRECVID Video Summarization, pp. 25–29. ACM (2007)

    Google Scholar 

  28. Guan, G., Wang, Z., Lu, S., Deng, J.D., Feng, D.D.: Keypoint-based keyframe selection. IEEE Trans. Circuits Syst. Video Technol. 23(4), 729–734 (2013)

    Article  Google Scholar 

  29. Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., Theodoridis, Y.: Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans. Knowl. Data Eng. 24(7), 1328–1343 (2012)

    Article  Google Scholar 

  30. Cahuina, E.J., Chavez, C.G.: A new method for static video summarization using local descriptors and video temporal segmentation. In: 26th SIBGRAPI-Conference on Graphics, Patterns and Images (SIBGRAPI), 2013, pp. 226–233. IEEE (2013)

    Google Scholar 

  31. Patel, A., Kasat, D., Jain, S., Thakare, V.: Performance analysis of various feature detector and descriptor for real-time video based face tracking. Int. J. Comp. Appl. 93(1), 37–41 (2014)

    Google Scholar 

  32. Kapela, R., McGuinness, K., Swietlicka, A., Oconnor, N.E.: Real-time event detection in field sport videos. In: Computer Vision in Sports, pp. 293–316. Springer (2014)

    Google Scholar 

  33. Khvedchenia, I.: A battle of three descriptors: surf, freak and brisk. Computer Vision Talks

    Google Scholar 

  34. Uijlings, J.R., Smeulders, A.W., Scha, R.J.: Real-time visual concept classification. IEEE Trans. Multimedia 12(7), 665–681 (2010)

    Article  Google Scholar 

  35. Li, J.: Video shot segmentation and key frame extraction based on sift feature. In: 2012 International Conference on Image Analysis and Signal Processing (IASP), pp. 1–8. IEEE (2012)

    Google Scholar 

  36. Papadopoulos, D.P., Chatzichristofis, S.A., Papamarkos, N.: Video summarization using a self-growing and self-organized neural gas network. In: Computer Vision/Computer Graphics Collaboration Techniques, pp. 216–226. Springer (2011)

    Google Scholar 

  37. Lux, M., Schöffmann, K., Marques, O., Böszörmenyi, L.: A novel tool for quick video summarization using keyframe extraction techniques. In: Proceedings of the 9th Workshop on Multimedia Metadata (WMM 2009). CEUR Workshop Proceedings, vol. 441, pp. 19–20 (2009)

    Google Scholar 

  38. Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: Computer Vision Systems, pp. 312–322. Springer (2008)

    Google Scholar 

  39. Chatzichristofis, S., Boutalis, Y.S., et al.: Fcth: Fuzzy color and texture histogram-a low level feature for accurate image retrieval. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008. WIAMIS’08, pp. 191–196. IEEE (2008)

    Google Scholar 

  40. Chatzichristofis, S.A., Boutalis, Y.S.: Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor. Multimedia Tools Appl. 46(2–3), 493–519 (2010)

    Article  Google Scholar 

  41. Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: Spcd-spatial color distribution descriptor-a fuzzy rule based compact composite descriptor appropriate for hand drawn color sketches retrieval. In: ICAART (1), pp. 58–63 (2010)

    Google Scholar 

  42. Bhaumik, H., Bhattacharyya, S., Das, M., Chakraborty, S.: Enhancement of Perceptual Quality in Static Video Summarization Using Minimal Spanning Tree Approach. In: 2015 International Conference on Signal Processing, Informatics, Communication and Energy Systems (IEEE SPICES), pp. 1–7. IEEE (2015)

    Google Scholar 

  43. Liu, D., Shyu, M.L., Chen, C., Chen, S.C.: Integration of global and local information in videos for key frame extraction. In: 2010 IEEE International Conference on Information Reuse and Integration (IRI), pp. 171–176. IEEE (2010)

    Google Scholar 

  44. Qian, Y., Kyan, M.: Interactive user oriented visual attention based video summarization and exploration framework. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5. IEEE (2014)

    Google Scholar 

  45. Qian, Y., Kyan, M.: High definition visual attention based video summarization. In: VISAPP, vol. 1, pp. 634–640 (2014)

    Google Scholar 

  46. Zhuang, Y., Rui, Y., Huang, T.S., Mehrotra, S.: Adaptive key frame extraction using unsupervised clustering. In: 1998 International Conference on Image Processing, 1998. ICIP 98. Proceedings, vol. 1, pp. 866–870. IEEE (1998)

    Google Scholar 

  47. Gong, Y., Liu, X.: Video summarization and retrieval using singular value decomposition. Multimedia Syst. 9(2), 157–168 (2003)

    Article  Google Scholar 

  48. Mundur, P., Rao, Y., Yesha, Y.: Keyframe-based video summarization using delaunay clustering. Int. J. Digit. Libr. 6(2), 219–232 (2006)

    Article  Google Scholar 

  49. Wan, T., Qin, Z.: A new technique for summarizing video sequences through histogram evolution. In: 2010 International Conference on Signal Processing and Communications (SPCOM), pp. 1–5. IEEE (2010)

    Google Scholar 

  50. Cayllahua-Cahuina, E., Cámara-Chávez, G., Menotti, D.: A static video summarization approach with automatic shot detection using color histograms. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p. 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2012)

    Google Scholar 

  51. Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: Stimo: still and moving video storyboard for the web scenario. Multimedia Tools Appl. 46(1), 47–69 (2010)

    Article  Google Scholar 

  52. de Avila, S.E.F., Lopes, A.P.B., da Luz, A., de Albuquerque Araújo, A.: Vsumm: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32(1), 56–68 (2011)

    Google Scholar 

  53. Xie, X.N., Wu, F.: Automatic video summarization by affinity propagation clustering and semantic content mining. In: 2008 International Symposium on Electronic Commerce and Security, pp. 203–208. IEEE (2008)

    Google Scholar 

  54. Liu, Z., Zavesky, E., Shahraray, B., Gibbon, D., Basso, A.: Brief and high-interest video summary generation: evaluating the at&t labs rushes summarizations. In: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, pp. 21–25. ACM (2008)

    Google Scholar 

  55. Ren, J., Jiang, J., Eckes, C.: Hierarchical modeling and adaptive clustering for real-time summarization of rush videos in trecvid’08. In: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, pp. 26–30. ACM (2008)

    Google Scholar 

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

    Article  Google Scholar 

  57. Gao, Y., Dai, Q.H.: Clip based video summarization and ranking. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 135–140. ACM (2008)

    Google Scholar 

  58. Tian, Z., Xue, J., Lan, X., Li, C., Zheng, N.: Key object-based static video summarization. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1301–1304. ACM (2011)

    Google Scholar 

  59. Liu, X., Song, M., Zhang, L., Wang, S., Bu, J., Chen, C., Tao, D.: Joint shot boundary detection and key frame extraction. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2565–2568. IEEE (2012)

    Google Scholar 

  60. Casella, G., George, E.I.: Explaining the gibbs sampler. Am. Stat. 46(3), 167–174 (1992)

    MathSciNet  Google Scholar 

  61. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

  62. Aggarwal, A., Biswas, S., Singh, S., Sural, S., Majumdar, A.K.: Object tracking using background subtraction and motion estimation in mpeg videos. In: Computer Vision-ACCV 2006, pp. 121–130. Springer (2006)

    Google Scholar 

  63. Pritch, Y., Ratovitch, S., Hende, A., Peleg, S.: Clustered synopsis of surveillance video. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. AVSS’09, pp. 195–200. IEEE (2009)

    Google Scholar 

  64. Kokare, M., Chatterji, B., Biswas, P.: Comparison of similarity metrics for texture image retrieval. In: TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region, vol. 2, pp. 571–575. IEEE (2003)

    Google Scholar 

  65. Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262–282 (2007)

    Article  MATH  Google Scholar 

  66. Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color-and texture-based image segmentation using em and its application to content-based image retrieval. In: Sixth International Conference on Computer Vision, 1998, pp. 675–682. IEEE (1998)

    Google Scholar 

  67. Szeliski, R.: Foundations and trends in computer graphics and vision. Found. Trends Comput. Graphics Vis. 2(1), 1–104 (2007)

    Article  Google Scholar 

  68. Marzotto, R., Fusiello, A., Murino, V.: High resolution video mosaicing with global alignment. In: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer

    Google Scholar 

  69. Matsushita, Y., Ofek, E., Ge, W., Tang, X., Shum, H.Y.: Full-frame video stabilization with motion inpainting. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1150–1163 (2006)

    Article  Google Scholar 

  70. Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  71. Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 797–819 (2011)

    Article  Google Scholar 

  72. Lee, J.H., Kim, W.Y.: Video summarization and retrieval system using face recognition and mpeg-7 descriptors. In: Image and Video Retrieval, pp. 170–178. Springer (2004)

    Google Scholar 

  73. Fatemi, N., Khaled, O.A.: Indexing and retrieval of tv news programs based on mpeg-7. In: International Conference on Consumer Electronics, 2001. ICCE, pp. 360–361. IEEE (2001)

    Google Scholar 

  74. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  75. Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 2, p. II-506. IEEE (2004)

    Google Scholar 

  76. Azad, P., Asfour, T., Dillmann, R.: Combining harris interest points and the sift descriptor for fast scale-invariant object recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009, pp. 4275–4280. IEEE (2009)

    Google Scholar 

  77. Khosla, A., Hamid, R., Lin, C.J., Sundaresan, N.: Large-scale video summarization using web-image priors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2698–2705. IEEE (2013)

    Google Scholar 

  78. Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 978–994 (2011)

    Article  Google Scholar 

  79. Pandey, R.C., Singh, S.K., Shukla, K., Agrawal, R.: Fast and robust passive copy-move forgery detection using surf and sift image features. In: 2014 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE (2014)

    Google Scholar 

  80. Yuan, Z., Lu, T., Wu, D., Huang, Y., Yu, H.: Video summarization with semantic concept preservation. In: Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia, pp. 109–112. ACM (2011)

    Google Scholar 

  81. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  82. Struzik, Z.R., Siebes, A.: The haar wavelet transform in the time series similarity paradigm. In: Principles of Data Mining and Knowledge Discovery, pp. 12–22. Springer (1999)

    Google Scholar 

  83. Sathyadevan, S., Balakrishnan, A.K., Arya, S., Athira Raghunath, S.: Identifying moving bodies from cctv videos using machine learning techniques. In: 2014 First International Conference on Networks & Soft Computing (ICNSC), pp. 151–157. IEEE (2014)

    Google Scholar 

  84. Bhaumik, H., Bhattacharyya, S., Dutta, S., Chakraborty, S.: Towards redundancy reduction in storyboard representation for static video summarization. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 344–350. IEEE (2014)

    Google Scholar 

  85. Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)

    Article  Google Scholar 

  86. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  87. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  88. Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings 3rd IEEE Workshop on Applications of Computer Vision, 1996. WACV’96., pp. 96–102. IEEE (1996)

    Google Scholar 

  89. Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.M.: Modeling and recognition of landmark image collections using iconic scene graphs. In: Computer Vision-ECCV 2008, pp. 427–440. Springer (2008)

    Google Scholar 

  90. Sikirić, I., Brkić, K., Šegvić, S.: Classifying traffic scenes using the gist image descriptor (2013). arXiv preprint arXiv:1310.0316

  91. Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Trans. Graphics (TOG) 26(3), 4 (2007)

    Article  Google Scholar 

  92. Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  93. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in neural information processing systems, pp. 1753–1760 (2009)

    Google Scholar 

  94. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. Comput. Vis.-ECCV 2010, 778–792 (2010)

    Google Scholar 

  95. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)

    Google Scholar 

  96. Xie, S., Zhang, W., Ying, W., Zakim, K.: Fast detecting moving objects in moving background using orb feature matching. In: 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pp. 304–309. IEEE (2013)

    Google Scholar 

  97. Bhaumik, H., Bhattacharyya, S., Chakraborty, S.: Video shot segmentation using spatio-temporal fuzzy hostility index and automatic threshold. In: 2014 Fourth International Conference on Communication Systems and Network Technologies (CSNT), pp. 501–506. IEEE (2014)

    Google Scholar 

  98. Bhattacharyya, S., Maulik, U., Dutta, P.: High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field. Appl. Soft Comput. 9(1), 126–134 (2009)

    Article  Google Scholar 

  99. De Avila, S.E., da Luz, A., de Araujo, A., Cord, M.: Vsumm: an approach for automatic video summarization and quantitative evaluation. In: XXI Brazilian Symposium on Computer Graphics and Image Processing, 2008. SIBGRAPI’08, pp. 103–110. IEEE (2008)

    Google Scholar 

  100. De Avila, S.E., da Luz Jr, A., De Araujo, A., et al.: Vsumm: A simple and efficient approach for automatic video summarization. In: 15th International Conference on Systems, Signals and Image Processing, 2008. IWSSIP 2008, pp. 449–452. IEEE (2008)

    Google Scholar 

  101. Liu, X., Mei, T., Hua, X.S., Yang, B., Zhou, H.Q.: Video collage. In: Proceedings of the 15th international conference on Multimedia, pp. 461–462. ACM (2007)

    Google Scholar 

  102. Liu, T., Zhang, X., Feng, J., Lo, K.T.: Shot reconstruction degree: a novel criterion for key frame selection. Pattern Recogn. Lett. 25(12), 1451–1457 (2004)

    Article  Google Scholar 

  103. Lee, H.C., Kim, S.D.: Iterative key frame selection in the rate-constraint environment. Sign. Process. Image Commun. 18(1), 1–15 (2003)

    Article  Google Scholar 

  104. Liu, R., Kender, J.R.: An efficient error-minimizing algorithm for variable-rate temporal video sampling. In: 2002 IEEE International Conference on Multimedia and Expo, 2002. ICME’02. Proceedings, vol. 1, pp. 413–416. IEEE (2002)

    Google Scholar 

  105. Chang, H.S., Sull, S., Lee, S.U.: Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circuits Syst. Video Technol. 9(8), 1269–1279 (1999)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, India

    Hrishikesh Bhaumik & Siddhartha Bhattacharyya

  2. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Susanta Chakraborty

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  1. Hrishikesh Bhaumik
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  2. Siddhartha Bhattacharyya
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Correspondence to Hrishikesh Bhaumik .

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  1. Department of Computer Science, Luleå University of Technology, Luleå, Sweden

    Ali Ismail Awad

  2. Image and Video Processing Lab, Faculty, South Valley University, Qena, Egypt

    Mahmoud Hassaballah

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Bhaumik, H., Bhattacharyya, S., Chakraborty, S. (2016). Redundancy Elimination in Video Summarization. In: Awad, A., Hassaballah, M. (eds) Image Feature Detectors and Descriptors . Studies in Computational Intelligence, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-319-28854-3_7

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