Research on the Multiple Feature Fusion Image Retrieval Algorithm based on Texture Feature and Rough Set Theory
This addresses image retrieval efficiency and robustness for users handling large-scale databases, though it appears incremental in nature.
The paper tackles the problem of retrieving images from large databases with complex content by proposing a multiple feature fusion algorithm combining texture features and rough set theory. The method improves overall accuracy compared to state-of-the-art algorithms, as verified by numerical experiments.
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.