Probabilistic spatial context models for scene content understanding

  • Authors:

  • Arnit Singhal;liebo Luo;Weiyu Zhu

  • Affiliations:

  • Electronic Imaging Products, R&D, Eastman Kodak Company;Electronic Imaging Products, R&D, Eastman Kodak Company;Dept. of Elec. Engg. And Compo Science, Univ. of Illinois, Urbana-Champaign

  • Venue:
  • CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2003

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Abstract

Scene content understanding facilitates a large number of applications, ranging from content-based image retrieval to other multimedia applications. Material detection refers to the problem of identifying key semantic material types (such as sky, grass, foliage, water, and snow in images. In this paper, we present a holistic approach to determining scene content, based on a set of individual material detection algorithms, as well as probabilistic spatial context models. A major limitation of individual material detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various material types. We have developed a spatial context-aware material detection system that reduces misclassification by constraining the beliefs to conform to the probabilistic spatial context models. Experimental results show that the accuracy of materials detection is improved by 13% using the spatial context models over the individual material detectors themselves.