Recently, we have witnessed the explosive growth of images with complexinformation and content. In order to effectively and precisely retrieve desiredimages from a large-scale image database with low time-consuming, we proposethe multiple feature fusion image retrieval algorithm based on the texturefeature and rough set theory in this paper. In contrast to the conventionalapproaches that only use the single feature or standard, we fuse the differentfeatures with operation of normalization. The rough set theory will assist usto enhance the robustness of retrieval system when facing with incomplete datawarehouse. To enhance the texture extraction paradigm, we use the wavelet Gaborfunction that holds better robustness. In addition, from the perspectives ofthe internal and external normalization, we re-organize extracted feature withthe better combination. The numerical experiment has verified generalfeasibility of our methodology. We enhance the overall accuracy compared withthe other state-of-the-art algorithms.
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