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Deep Hashing for Semi-supervised Content Based Image Retrieval

  • Bashir, Muhammad Khawar (Department of Computer Science & Engineering, University of Engineering and Technology) ;
  • Saleem, Yasir (Department of Computer Science & Engineering, University of Engineering and Technology)
  • Received : 2017.10.23
  • Accepted : 2018.04.02
  • Published : 2018.08.31

Abstract

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

Keywords

References

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