  Recently, we have witnessed the explosive growth of images with complex
information and content. In order to effectively and precisely retrieve desired
images from a large-scale image database with low time-consuming, we propose
the multiple feature fusion image retrieval algorithm based on the texture
feature and rough set theory in this paper. In contrast to the conventional
approaches that only use the single feature or standard, we fuse the different
features with operation of normalization. The rough set theory will assist us
to enhance the robustness of retrieval system when facing with incomplete data
warehouse. To enhance the texture extraction paradigm, we use the wavelet Gabor
function that holds better robustness. In addition, from the perspectives of
the internal and external normalization, we re-organize extracted feature with
the better combination. The numerical experiment has verified general
feasibility of our methodology. We enhance the overall accuracy compared with
the other state-of-the-art algorithms.
