Research on the Multiple Feature Fusion Image Retrieval Algorithm based on Texture Feature and Rough Set Theory

TL;DR

This paper introduces a novel image retrieval algorithm that fuses multiple features, including texture and rough set theory, to improve accuracy and robustness in large-scale image databases.

Contribution

The paper proposes a new multi-feature fusion method combining texture features and rough set theory, enhancing retrieval accuracy and robustness against incomplete data.

Findings

Improved retrieval accuracy over existing algorithms

Enhanced robustness with incomplete data using rough set theory

Effective texture feature extraction with wavelet Gabor functions

Abstract

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.