Deep learning of binary hash codes for fast image retrieval

  • Lin K
  • Yang H
  • Hsiao J
 et al. 
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Abstract

Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effective deep learning framework to generate binary hash codes for fast image retrieval. Our idea is that when the data labels are available, binary codes can be learned by employing a hidden layer for representing the latent concepts that dominate the class labels. The utilization of the CNN also allows for learning image representations. Unlike other supervised methods that require pair-wised inputs for binary code learning, our method learns hash codes and image representations in a point-wised manner, making it suitable for large-scale datasets. Experimental results show that our method outperforms several state-of-the-art hashing algorithms on the CIFAR-10 and MNIST datasets. We further demonstrate the scalability and efficacy of the proposed approach on the large-scale dataset of 1 million clothing images.

Author-supplied keywords

  • Binary codes
  • Image representation
  • Image retrieval
  • Machine learning
  • Neurons
  • Semantics
  • Visualization

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