Deep Supervised Discrete Hashing

Abstract

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification. Both the pairwise label information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithm. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our method outperforms current state-of-the-art methods on benchmark datasets.

Accepted by NIPS 2017

References in corpus (6)

• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
• Return of the Devil in the Details: Delving Deep into Convolutional Nets
• Fast Supervised Hashing with Decision Trees for High-Dimensional Data
• Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks
• Supervised Discrete Hashing
• Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval

Cited by in corpus (12)

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• Efficient end-to-end learning for quantizable representations
• CIMON: Towards High-quality Hash Codes
• End-to-End Supervised Product Quantization for Image Search and Retrieval
• NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
• Asymmetric Deep Semantic Quantization for Image Retrieval
• Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
• Object Detection based Deep Unsupervised Hashing
• Pairwise Teacher-Student Network for Semi-Supervised Hashing