基于模糊粗糙集的图像自动分类研究

Automatic image classification based on fuzzy-rough set

如何使用图像底层特征有效表达高层语义是实现图像自动分类难以逾越的鸿沟。本文将模糊粗糙集理论引入图像自动分类,在使用图像底层特征表达高层语义的图像自动分类过程中,把高维特征向量处理、合适的描述符集合选择难题转换为模糊决策表,使用图像语义贴近度概念来检验图像特征属性间的数据依赖关系,以达到属性约简,剔除冗余信息和图像分类规则推导的目的,并定义了图像类别隶属度函数对图像进行分类。实验结果表明该图像分类系统的分类正确率达81.7%,说明该方法具有很好的精确性和有效性,能较好地实现图像自动分类。

There is a gap between low-level feature of image and high-level semantic understanding of users in the automatic image classification.The fuzzy-rough set theory was introduced into automatic image classification.During the mapping from low-level visual feature to high-level semantic feature,the problems of high-dimensional feature vector processing and the appropriate choice of descriptors in image classification processing were converted to fuzzy decision table,and the concept of the semantic proximity was used to verify the dependent relations of image attributes for attribute reduction,which could eliminate the redundant information and deduce the rule of image classification.In the end,the category of an image was determined by a membership degree function.An image classification system was developed and the accuracy of the classification results was 81.7%,experimental results show that the method has good accuracy and effectiveness,and can achieve a better image automatic classification.