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中文摘要：
提出一种改进的语义物体分类方法,可在图像区域分割结果不精确的情况下,对图像区域进行分类.结合统计文本分析中的bag-of-words方法与多示例学习,将图像区域物体切分为若干小图像块,提取图像区域物体的图像特征作为其粗糙语义概念并计算置信度;根据粗糙语义概念进一步提取出各种区域物体类型的语义特征作为其特征语义概念;使用分类器对特征语义概念进行学习,实现了对区域物体的分类.实验结果表明,采用文中方法可在分割粗糙的图像区域上获得很好的区域物体分类准确率.
英文摘要：
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.
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