Unsupervised Video Hashing via Deep Neural Network

Hashing is a common solution for content-based multimedia retrieval by encoding high-dimensional feature vectors into short binary codes. Previous works mainly focus on image hashing problem. However, these methods can not be directly used for video hashing, as videos contain not only spatial structure within each frame, but also temporal correlation between successive frames. Several researchers proposed to handle this by encoding the extracted key frames, but these frame-based methods are time-consuming in real applications. Other researchers proposed to characterize the video by averaging the spatial features of frames and then the existing hashing methods can be adopted. Unfortunately, the sort of “video” features does not take the correlation between frames into consideration and may lead to the loss of the temporal information. Therefore, in this paper, we propose a novel unsupervised video hashing framework via deep neural network, which performs video hashing by incorporating the temporal structure as well as the conventional spatial structure. Specially, the spatial features of videos are obtained by utilizing convolutional neural network, and the temporal features are established via long-short term memory. After that, the time series pooling strategy is employed to obtain the single feature vector for each video. The obtained spatio-temporal feature can be applied to many existing unsupervised hashing methods. Experimental results on two real datasets indicate that by employing the spatio-temporal features, our hashing method significantly improves the performance of existing methods which only deploy the spatial features, and meanwhile obtains higher mean average precision compared with the state-of-the-art video hashing methods.