Abstract:
Natural object detection in outdoor scenes, i.e., identifying key object types such as sky, grass, foliage, water, and snow, can facilitate content-based applications, ra...Show MoreMetadata
Abstract:
Natural object detection in outdoor scenes, i.e., identifying key object types such as sky, grass, foliage, water, and snow, can facilitate content-based applications, ranging from image enhancement to other multimedia applications. A major limitation of individual object detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various object types and lack of context information. We have developed a spatial context-aware object-detection system that first combines the output of individual object detectors to produce a composite belief vector for the objects potentially present in an image. Spatial context constraints, in the form of probability density functions obtained by learning, are subsequently used to reduce misclassification by constraining the beliefs to conform to the spatial context models. Experimental results show that the spatial context models improve the accuracy of natural object detection by 13% over the individual object detectors themselves.
Published in: 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)
Date of Conference: 06-09 July 2003
Date Added to IEEE Xplore: 18 August 2003
Print ISBN:0-7803-7965-9