Region Objects Classification with Coarse Image Region Segmentation

An improved image region classification method is presented in this paper, which can works with inaccurate segmentation of image regions.First, each coarsely segmented region was divided into several sub-blocks by using bag-of-words method, and the semantic features of the region were obtained by clustering the low-level image features extracted from these sub-blocks.According to the confidence of the semantic features, these semantic features were further refined by using multi-instance learning method.Finally, these refined semantic features were learned by a SVM classifier for region classification.The experimental results show that the proposed method achieves good region classification with the coarse region segmentation of the image.