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What Visual Attributes Characterize an Object Class?

  • Jianlong FuEmail author
  • Jinqiao Wang
  • Xin-Jing Wang
  • Yong Rui
  • Hanqing Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9003)

Abstract

Visual attribute-based learning has shown a big impact on many computer vision problems in recent years. Albeit its usefulness, most of works only focus on predicting either the presence or the strength of pre-defined attributes. In this paper, we discuss how to automatically learn visual attributes that characterize an object class. Starting from the images of an object class that are collected from the Web, we first mine visual prototypes of attributes (i.e., a clean intermediate representation for learning attributes) by clustering with Gaussian mixtures from multi-scale salient areas in noisy Web images. Second, a joint optimization model is proposed to fulfill the attribute learning with feature selection. As sparse approximation is adopted for feature selection during the joint optimization, the learned attributes tend to present a more representative visual property, e.g., stripe pattern (when texture features are selected), yellow-color (when color features are selected). Finally, to quantify the confidence of attributes and restrain the noisy attributes learned from the Web, a ranking-based method is proposed to refine the learned attributes. Our approach ensures the learned visual attributes to be visually recognizable and representative, in contrast to manually constructed attributes [1] that contain properties difficult to be visualized, e.g., “smelly,” “smart.” We evaluated our approach on two benchmark datasets, and compared with state-of-the-art approaches in two aspects: the quality of the learned visual attributes and their effectiveness in object categorization.

Keywords

Gaussian Mixture Model Object Class Object Categorization Rank Score Visual Attribute 
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.

Notes

Acknowledgement

This work was supported by 863 Program (2014AA015104), and National Natural Science Foundation of China (61273034, and 61332016).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jianlong Fu
    • 1
    Email author
  • Jinqiao Wang
    • 1
  • Xin-Jing Wang
    • 2
  • Yong Rui
    • 2
  • Hanqing Lu
    • 1
  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Microsoft ResearchBeijingChina

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