Aiming at overcoming the semantic gap between low-level features and high-level semantic concepts of images and to improve the precision of image classification, we propose an approach to construct a color semantic feature to map low-level features to semantically meaningful categories. Color features are extracted first and then a semantic network containing a set of color concepts and related objects is build. Finally, a color semantic 3-tuple is being constructed. The proposed method can not only get better results in image classification, but is also more robust.%为了弥补图像底层特征到高层语义之间的语义鸿沟,提出一种颜色语义特征的构建方法以建立新的语义映射来提高图像分类准确率.通过提取底层颜色特征,构建包含颜色概念的语义网络,建立了颜色语义特征三元组,利用机器学习分类算法进行图像分类.实验结果表明,利用文章提出的新方法构建的语义特征向量进行图像分类,不仅可以取得优秀的分类结果,同时对不同的分类算法具有鲁棒性.
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