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Describing Clothing by Semantic Attributes

  • Huizhong Chen
  • Andrew Gallagher
  • Bernd Girod
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system.

Keywords

Semantic Attribute Sift Descriptor Attribute Prediction Gender Recognition Solid Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huizhong Chen
    • 1
  • Andrew Gallagher
    • 2
    • 3
  • Bernd Girod
    • 1
  1. 1.Department of Electrical EngineeringStanford UniversityStanfordUSA
  2. 2.Kodak Research Laboratories, RochesterUSA
  3. 3.Cornell UniversityIthacaUSA

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