Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames
Abstract The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.
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Zhang, C., Y. Lin, L. Zhu, A. Liu, Z. Zhang, and F. Huang, 2019. CNN-VWII: An efficient approach for large-scale video retrieval by image queries. Pattern Recognition Letters, 123, 82–88.
Bolettieri, P., F. Carrara, F. Debole, F. Falchi, C. Gennaro, L. Vadicamo, and C. Vairo, 2019. An image retrieval system for video. In International Conference on Similarity Search and Applications (pp. 332–339). Springer, Cham.
Chen, X., Y. Zhang, Q. Ai, H. Xu, J. Yan, and Z. Qin. 2017. Personalized key frame recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 315–324). ACM.
El Ouadrhiri, A. A., E. M. Saoudi, S. J. Andaloussi, O. Ouchetto, and A. Sekkaki. 2017. Content based video retrieval based on bounded coordinate of motion histogram. In 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 0573–0578). IEEE.
Han, S, J, Pool, J. Tran and W. Dally, 2015. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems (pp. 1135–1143).
Iqbal, S., A. N. Qureshi, and A. M. Lodhi. 2018. Content Based Video Retrieval Using Convolutional Neural Network. In Proceedings of SAI Intelligent Systems Conference (pp. 170–186). Springer, Cham.
Jones, S., and L. Shao. 2013. Content-based retrieval of human actions from realistic video databases. Information Sciences, 236, 56–65.
Lingam, K. M., and V. S. K. Reddy. 2019. Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval. In Soft Computing and Signal Processing (pp. 811–820). Springer, Singapore.
Mathieu, M. C. Couprie, and Y. LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv: 1511.05440.
Rossetto, L., I. Giangreco, H. Schuldt, S. Dupont, O. Seddati, M. Sezgin, and Y. Sahillio?lu. 2015. IMOTION—a content-based video retrieval engine. In International Conference on Multimedia Modeling (pp. 255–260). Springer, Cham.
Sedighi, V., and J. Fridrich. 2017. Histogram layer, moving convolutional neural networks towards feature-based steganalysis. Electronic Imaging, 2017(7), 50–55.
Sikos, L. F. 2018. Ontology-based structured video annotation for content-based video retrieval via spatiotemporal reasoning. In Bridging the Semantic Gap in Image and Video Analysis (pp. 97–122). Springer, Cham.
Song, J., H. Zhang, X. Li, L. Gao, M. Wang, and R. Hong. 2018. Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Transactions on Image Processing, 27(7), 3210–3221.
Thanh, T. M.,P.T Hiep, T.M Tam and K. Tanaka, 2014. Robust semi-blind video watermarking based on frame-patch matching. AEU-International Journal of Electronics and Communications, 68(10), 1007–1015.
Tarigan, J. T., and E. P. Marpaung. 2018. Implementing Content Based Video Retrieval Using Speeded-Up Robust Features. International Journal of Simulation–Systems, Science & Technology, 19(3).
Zhang, C., Y. Lin, L. Zhu, A. Liu, Z. Zhang, and F. Huang, 2019. CNN-VWII: An efficient approach for large-scale video retrieval by image queries. Pattern Recognition Letters, 123, 82–88.
Hussain, A., Ahmad, M., Hussain, T., & Ullah, I. (2022). Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 207–235. https://doi.org/10.14201/adcaij.27430
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