Supervised Hashing for Image Retrieval via Image Representation Learning

Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of hand-crafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically…

• NS
  National Science Foundation
• NN
  National Natural Science Foundation of China

  Awards: 61370021, 61003241
• NS
  Natural Science Foundation of Guangdong Province

  Award: S2013010011905