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Content Based Video Retrieval in Transformed Domain using Fractional Coefficients
H.B.Kekre, Sudeep D. Thepade, Saurabh Gupta
Pages - 237 - 247     |    Revised - 15-05-2013     |    Published - 30-06-2013
Volume - 7   Issue - 3    |    Publication Date - June 2013  Table of Contents
Key Frame, Feature Extraction, Similarity Measures, Orthogonal Transforms.
With the development of multimedia and growing database there is huge demand of video retrieval systems. Due to this, there is a shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval. Good features selection also allows the time and space costs of the retrieval process to be reduced. Different methods[1,2,3] have been proposed to develop video retrievals systems to achieve better performance in terms of accuracy.
The proposed technique uses transforms to extract the features. The used transforms are Discrete Cosine, Walsh, Haar, Kekre, Discrete Sine, Slant and Discrete Hartley transforms. The benefit of energy compaction of transforms in higher coefficients is taken to reduce the feature vector size by taking fractional coefficients[5] of transformed frames of video. Smaller feature vector size results in less time for comparison of feature vectors resulting in faster retrieval of images. The feature vectors are extracted and coefficients sets are considered as feature vectors (100%, 6.25%, 3.125%, 1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012%, 0.006% and 0.003% of complete transformed coefficients). The database consists of 500 videos spread across 10 categories.
CITED BY (12)  
1 Kekre, H. B., Mishra, D., & Rege, M. P. Survey on Recent Techniques in Content Based Video Retrieval.
2 Katkar, V., Kulkarni, S., & Bhatia, D. (2015, January). Traffic Video Classification using edge detection techniques. In Nascent Technologies in the Engineering Field (ICNTE), 2015 International Conference on (pp. 1-6). IEEE.
3 Patil, P. H., Thepade, S. D., & Sonare, B. (2015). Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures. International Journal of Computer Applications, 122(5).
4 Yadav, N., & Thepade, S. D. Self-Mutation of Hybrid Wavelet Transform with Cosine-Kekre, Cosine-Haar, Cosine-Walsh, Walsh-Cosine, Haar-Cosine and Kekre-Cosine for Content Based Video Retrieval.
5 Yadav, N., & Thepade, S. D. (2015). Comprehensive Performance Comparison of Fourier, Walsh, Haar, Sine and Cosine Transforms for Video Retrieval with Partial Coefficients of Transformed Video. International Journal of Computer Applications, 120(19).
6 Thepade, S. D., & Yadav, N. (2015, April). Partial energy of hybrid wavelet transformed videos for content based video retrieval with various similarity measures using Cosine, Haar and Walsh transforms. In Communication Technologies (GCCT), 2015 Global Conference on (pp. 261-266). IEEE.
7 Thepade, S., & Mandal, P. R. (2014). Novel Iris Recognition Techniques using Energy Compaction and Partial Energies of Transformed Iris Images with Cosine-Kekre and Cosine-Hartley Hybrid Wavelet Transforms. IJRCCT, 3(9), 961-964.
8 Tiwaskar, S. A., Rege, P. R., & Prasad, R. S. (2014). Development of Framework for Text Extraction from Videos. Digital Image Processing, 6(7), 295-299.
9 Cedillo-Hernandez, M., Garcia-Ugalde, F. J., Cedillo-Hernandez, A., Nakano-Miyatake, M., & Perez-Meana, H. (2014, November). Content Based Video Retrival System for Mexican Culture Heritage Based on Object Matching and Local-Global Descriptors. In Mechatronics, Electronics and Automotive Engineering (ICMEAE), 2014 International Conference on (pp. 38-43)
10 Zheng Haibo, Han Xiaoxuan, Shiyun Jing, Li Jie, & Zhuxiu Chang. (2014). Content-based multi-level mass surveillance video retrieval system. TV technology, 38 (19), 196-201.
11 Thepade, D. S., & Mandal, P. R. (2014). Novel Iris Recognition Technique using Fractional Energies of Transformed Iris Images using Haar and Kekre Transforms. International Journal Of Scientific & Engineering Research, 5(4).
12 Thepade, S., & Mandal, P. R. (2014, December). Energy compaction based novel Iris recognition techniques using partial energies of transformed iris images with Cosine, Walsh, Haar, Kekre, Hartley Transforms and their Wavelet Transforms. In India Conference (INDICON), 2014 Annual IEEE (pp. 1-6). IEEE.
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Dr. H.B.Kekre
Mukesh Patel School of Technology, Management & Engineering - India
Dr. Sudeep D. Thepade
Professor & Dean (R&D), Pimpri Chinchwad College of Engineering, University of Pune, Pune, India - India
Mr. Saurabh Gupta
Mukesh Patel School of Technology, Management, and Engineering - India