Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification

R. Zhang, L. Lin, R. Zhang, W. Zuo, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

454 Citations (Scopus)

Abstract

Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.
Original languageEnglish
Pages (from-to)4766-4779
Number of pages14
JournalIEEE Transactions on Image Processing
Volume24
Issue number12
DOIs
Publication statusPublished - 2015

Keywords

  • Deep model
  • Hashing learning
  • Image retrieval
  • Person re-identification
  • Similarity comparison

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

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