Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (4): 35-39.doi: 10.16180/j.cnki.issn1007-7820.2022.04.006

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Video Retrieval Algorithm Based on 3D Convolution and Hash Method

Hanqing CHEN,Feifei LI,Qiu CHEN   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 20093,China
  • Received:2020-11-24 Online:2022-04-15 Published:2022-04-15
  • Supported by:
    The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning(ES2015XX)


Different from other multimedia information retrieval, video retrieval requires a large amount of computation in similarity calculation due to the large amount of information contained in videos. In addition, the temporal correlation between video frames is often ignored in feature extraction, which leads to insufficient feature extraction and affects the accuracy of video retrieval. For this problem, this study proposes a video retrieval method based on 3D convolution and Hash method. This method constructs an end-to-end framework, uses a 3D convolutional neural network to extract the features of the representative frames selected from the video, and then maps the features to the low-dimensional Hamming space to calculate the similarity in the Hamming space. Experimental results on two video data sets show that compared with the latest video retrieval algorithms, the proposed method has a greater improvement in accuracy.

Key words: video retrieval, 3D convolution, feature representation, Hash method, supervised learning, feature reduction, Hamming space, similarity matching

CLC Number: 

  • TP391