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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 3 D convolution and Hash method. This method constructs an end-to-end framework, uses a 3 D 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. [ABSTRACT FROM AUTHOR]

The coding metliods of the traditional visiual features adopted in current image retrieval approaches lack sufficient learning ability and have no strong featiure expression ability. In addition，due to the high dimensionali¬ ty of visual features，a large amount of memory is consumed，thus reducing the performance of image retrieval. In this paper，an image retrieval algoritiim witii end - to - end training based on deep and improved hashiing metiiod was pro¬posed and designed. The proposed algorithm combined the high - level features extracted by CNNwith Hash function and learned Hashi codes witii expression ability to perform large - scale image retrieval in low - dimensional Hamming space. The experimental results on two main datasets showed that the retrieval performance of the proposed metiiod was superior to that of some state - of - the - art ones. [ABSTRACT FROM AUTHOR]

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