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

DOI

10.2991/icamcs-16.2016.61How to use a DOI?

Keywords

Feature Extraction, Image Retrieval, Rough Set, Feature Fusion, Algorithmatic Design, Large-scale Database, Content Based Method, Precision Accuracy.

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaojie Shi
AU  - Yijun Shao
PY  - 2016/06
DA  - 2016/06
TI  - Research on the Multiple Feature Fusion Image Retrieval Algorithm based on Texture Feature and Rough Set Theory
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 288
EP  - 292
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.61
DO  - 10.2991/icamcs-16.2016.61
ID  - Shi2016/06
ER  -