Abstract
Many algorithms have been propounded to retrieve videos from a huge database. Yet, they could not reduce the time consumption and their efficiency could completely not satisfy the users. Unlike the existing systems, the proposed approach integrates spatio-temporal features by exploiting the complete video information and it enhances the efficacy of video retrieval. In this paper, we extract color and motion features to obtain spatio-temporal features. We have employed HSV color histogram method for color feature extraction and motion histogram method for extracting video motion feature. Experimental results have shown better performance of these algorithms compared to the existing algorithms in video retrieval.
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References
Hu, Weiming, et al. “A survey on visual content-based video indexing and retrieval.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41.6 (2011): 797–819.
Patel, B. V., and B. B. Meshram. “Content based video retrieval systems.” arXiv:1205.1641 (2012).
Megrhi, Sameh, Wided Souidene, and Azeddine Beghdadi. “Spatio-temporal salient feature extraction for perceptual content based video retrieval.” Colour and Visual Computing Symposium (CVCS), 2013. IEEE, 2013.
Gao, Han-ping, and Zu-qiao Yang. “Content based video retrieval using spatiotemporal salient objects.” Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on. IEEE, 2010.
Zhao Guang-sheng, A Novel Approach for Shot Boundary Detection and Key Frames Extraction, 2008 International Conference on Multimedia and Information Technology, IEEE
Hannane, Rachida, et al. “An efficient method for video shot boundary detection and key frame extraction using SIFT-point distribution histogram.” International Journal of Multimedia Information Retrieval 5.2 (2016): 89–104.
Wu, Zhonglan, and Pin Xu. “Shot boundary detection in video retrieval.” Electronics Information and Emergency Communication (ICEIEC), 2013 IEEE 4th International Conference on. IEEE, 2013.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110, 2004.
Ren, Liping, et al. “Key frame extraction based on information entropy and edge matching rate.” Future Computer and Communication (ICFCC), 2010 2nd International Conference on. Vol. 3. IEEE, 2010.
Lina Sun and Yihua Zhou, “A key frame extraction method based on mutual information and image entropy,” 2011 International Conference on Multimedia Technology, Hangzhou, 2011, pp. 35–38.
Daga, Brijmohan. “Content based video retrieval using color feature: an integration approach.” In Advances in Computing, Communication, and Control, pp. 609–625. Springer, Berlin, Heidelberg, 2013.
Ma, Ji-quan. “Content-based image retrieval with HSV color space and texture features.” Web Information Systems and Mining, 2009. WISM 2009. International Conference on. IEEE, 2009.
Tahayna, Bashar, Mohammed Belkhatir, and Saadat Alhashmi. “Motion information for video retrieval.” Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on. IEEE, 2009.
Yi, Haoran, Deepu Rajan, and Liang-Tien Chia. “A new motion histogram to index motion content in video segments.” Pattern Recognition Letters 26.9 (2005): 1221–1231.
Chun, Young Deok, Nam Chul Kim, and Ick Hoon Jang. “Content-based image retrieval using multiresolution color and texture features.” IEEE Transactions on Multimedia 10, no. 6 (2008): 1073–1084.
Hu, Rui, Stuart James, and John Collomosse. “Annotated free-hand sketches for video retrieval using object semantics and motion.” Advances in Multimedia Modeling (2012), Springer: 473–484.
Malik, Fazal, and Baharum Baharudin. “Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain.” Journal of king saud university-computer and information sciences 25.2 (2013): 207–218.
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Kumar, G., Reddy, V., Srinivas Kumar, S. (2018). High-Performance Video Retrieval Based on Spatio-Temporal Features. In: Anguera, J., Satapathy, S., Bhateja, V., Sunitha, K. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-10-7329-8_44
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DOI: https://doi.org/10.1007/978-981-10-7329-8_44
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