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Probabilistic spatial context models for scene content understanding | IEEE Conference Publication | IEEE Xplore

Probabilistic spatial context models for scene content understanding


Abstract:

Scene content understanding facilitates a large number of applications, ranging from content-based image retrieval to other multimedia applications. Material detection re...Show More

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.
Date of Conference: 18-20 June 2003
Date Added to IEEE Xplore: 15 July 2003
Print ISBN:0-7695-1900-8
Print ISSN: 1063-6919
Conference Location: Madison, WI, USA

References

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