Learning Compact Spatio-Temporal Features for Fast Content based Video Retrieval
Vidit Kumar1, Vikas Tripathi2, Bhaskar Pant3
1Vidit Kumar*, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
2Vikas Tripathi, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
3Bhaskar Pant, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 21 November, 2019. | Manuscript published on December 10, 2019. | PP: 2404-2409 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7847129219/2019©BEIESP | DOI: 10.35940/ijitee.B7847.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Videos are recorded and uploaded daily to the sites like YouTube, Facebook etc. from devices such as mobile phones and digital cameras with less or without metadata (semantic tags) associated with it. This makes extremely difficult to retrieve similar videos based on this metadata without using content based semantic search. Content based video retrieval is problem of retrieving most similar videos to a given query video and has wide range of applications such as video browsing, content filtering, video indexing, etc. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a fast content based video retrieval framework using compact spatio-temporal features learned by deep learning. Specifically, deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. For fast retrieval, binary code is generated by hashing learning component in the framework. For fast and effective learning of hash code proposed framework is trained in two stages. First stage learns the video dynamics and in second stage compact code is learn using learned video’s temporal variation from the first stage. UCF101 dataset is used to test the proposed method and results compared by other hashing methods. Results show that our approach is able to improve the performance over existing methods.
Keywords: CNN, LSTM, Hashing, CBVR, Deep learning.
Scope of the Article: Deep learning.