This issue of image classification has received much attention recently; however, to classify huge amount of images into different categories is hard. For each category, classification system should first analyze features of image, which is described by visual words, and then the classification model is constructed. Finally, a probabilistic classifier is proposed to effectively category images. The proposed algorithm is first collected training samples from the database, and extract image feature by visual patch. In addition, the patch divided into macro words and micro words according to patch content and then macro and micro visual dictionary is constructed. We then build classification models with representative and effective. Finally, the MAP based classifier is developed to classification image correctly. Simulation results show that the categorization scheme achieve surprising performance.
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