Efficient clothing retrieval with semantic-preserving visual phrases

  • Authors:

  • Jianlong Fu;Jinqiao Wang;Zechao Li;Min Xu;Hanqing Lu

  • Affiliations:

  • Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Centre for Innovation in IT services and Applications, University of Technology, Sydney, Australia;Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2012

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Abstract

In this paper, we address the problem of large scale cross-scenario clothing retrieval with semantic-preserving visual phrases (SPVP). Since the human parts are important cues for clothing detection and segmentation, we firstly detect human parts as the semantic context, and refine the regions of human parts with sparse background reconstruction. Then, the semantic parts are encoded into the vocabulary tree under the bag-of-visual-word (BOW) framework, and the contextual constraint of visual words among different human parts is exploited through the SPVP. Moreover, the SPVP is integrated into the inverted index structure for accelerating the retrieval process. Experiments and comparisons on our clothing dataset indicate that the SPVP significantly enhances the discriminative power of local features with a slight increase of memory usage or runtime consumption compared to the BOW model. Therefore, the approach is superior to both the state-of-the-art approach and two clothing search engines.