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Sketch-Based Image Retrieval via Compact Binary Codes Learning

  • Xinhui Wu
  • Shuangjiu Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

With the exploding number of images on the Internet and the convenience of free-hand sketch drawing, sketch-based image retrieval (SBIR) has attracted much attention in recent years. Due to the ambiguity and sparsity of sketches, SBIR is more challenging to cope with than conventional content-based problem. Existing approaches usually adopt high-dimensional features which require high-computational cost. Furthermore, they often use edge detection and parameter-sharing networks which may lose important information in training. In this study, we propose a compact binary codes learning strategy using deep architecture. By leveraging well-designed prototype hash codes, we embed different domains input (sketch and photo) into a common comparable feature space. Besides, we present two separate networks specific to sketches and real photos which can learn very compact features in Hamming space. Our method achieves state-of-the-art results in accuracy, retrieval time and memory cost on two standard large-scale datasets.

Keywords

Deep learning Hashing Sketch-based image retrieval 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of SoftwareShanghai Jiao Tong UniversityShanghaiChina

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