Application of Rough Set in Image´s Feature Attributes Reduction

After PCA pre-processing, rough set theory was introduced in image´s feature attributes reduction, and its application in characterized parameters´ attribute optimization was explored. The combination of these two methods was effective in reducing the unnecessary attributes. The novel algorithm could also decrease the complexity of CBIR´s inner redundancy. The experimental result of attribute reduction using UCI dataset also indicated that there was in-built redundancy of the original features and the complexity of the follow-up processing had cut down through employing the methods mentioned in this paper.