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Title: Self-supervised video hashing with hierarchical binary auto-encoder
Authors: Song, Jingkuan
Zhang, Hanwang
Li, Xiangpeng
Gao, Lianli
Wang, Meng
Hong, Richang
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Song, J., Zhang, H., Li, X., Gao, L., Wang, M., & Hong, R. (2018). Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Transactions on Image Processing, 27(7), 3210-3221. doi:10.1109/TIP.2018.2814344
Journal: IEEE Transactions on Image Processing
Abstract: Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed self-supervised video hashing (SSVH), which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion. We specifically address two central problems: 1) how to design an encoder-decoder architecture to generate binary codes for videos and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world data sets show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the current best performance on the task of unsupervised video retrieval.
ISSN: 1057-7149
DOI: 10.1109/TIP.2018.2814344
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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