Probabilistic Classification Based Image Regions Labeling
Min Zhang, Yunde Jia · 2005
Image regions labeling is a crucial step of scene understanding. In this paper, we propose an approach that can solve the image regions labeling problem under the Bayesian probabilistic framework. In our approach, we first extract the efficient low-level visual features from the segmented image regions. Then we represent the knowledge of each label class as a Gaussian mixture model of image region features, and describe the relationship between different region label classes using likelihood function. Finally, the label of each image region is determined by probabilistic classification. The proposed approach is evaluated on the outdoor scene images and the overall recognition accuracy is over 86%.