This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval. We train our network in a supervised way by fully exploiting inter-class diversity and intra-class identity. Classification loss is optimized to maximize inter-class diversity, while intra-pair is introduced to learn representative intra-class identity. We investigate the binary bits distribution related to categories and find out that the effectiveness of binary bits is highly related to categories, and certain bits may degrade classification performance of some categories. We then design hash code generation scheme with category mask to filter out bits with negative contribution. Experimental results demonstrate the proposed method outperforms state-of-the-arts under various evaluation metrics on public datasets. We are making our code and models public online.
We propose to learn both video feature representation and hash functions within a deep learning pipeline. As far as we know, only one other approach has provided an end-to-end deep hash learning pipeline @cite_4 . Our approach differs from that work both in neural network structure and in supervision learning metric. We also investigate the influence of category on hash code bits distribution and devise a category mask based hash code generation method for efficient video retrieval.
@cite_4
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Retrieving faces from large mess of videos is an attractive research topic with wide range of applications. Its challenging problems are large intra-class variations, and tremendous time and space complexity. In this paper, we develop a new deep convolutional neural network (deep CNN) to learn discriminative and compact binary representations of faces for face video retrieval. The network integrates feature extraction and hash learning into a unified optimization framework for the optimal compatibility of feature extractor and hash functions. In order to better initialize the network, the low-rank discriminative binary hashing is proposed to pre-learn hash functions during the training procedure. Our method achieves excellent performances on two challenging TV-Series datasets.