Automatic image classification based on fuzzy-rough set

作者：Chen Zai Qing; Shi Jun Sheng^*; Bai Feng Xiang

来源：Journal of Jilin University (Engineering and Technology Edition), 2013, 43(SUPPL.1): 209-212.

• Automatic classification
• Automatic image classification
• Fuzzy-rough sets
• High-dimensional features
• High-level semantic features
• Image classification systems
• Membership degrees
• Redundant informations

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.

• 出版日期2013
• 单位云南师范大学