A relevance feedback framework for image retrieval based on ant colony algorithm

Authors:
Guang-Peng Chen;Yu-Bin Yang;Yao Zhang;Ling-Yan Pan;Yang Gao;Lin Shang
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Jinling College, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Year:
2011

Citing 6
Cited 0

MindReader: Querying Databases Through Multiple Examples

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Probabilistic semantic network-based image retrieval using MMM and relevance feedback

Multimedia Tools and Applications
Image retrieval: Ideas, influences, and trends of the new age

ACM Computing Surveys (CSUR)
Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback

IEEE Transactions on Multimedia
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A memory learning framework for effective image retrieval

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

To utilize users' relevance feedback is a significant and challenging issue in content-based image retrieval due to its capability of narrowing the "semantic gap" between the low-level features and the higher-level concepts. This paper proposes a novel relevance feedback framework for image retrieval based on Ant Colony algorithm, by accumulating users' feedback to construct a "hidden" semantic network and achieve a "memory learning" mechanism in image retrieval process. The proposed relevance feedback framework adopts both the generated semantic network and the extracted image features, and then re-weights them in similarity calculation to obtain more accurate retrieval results. Experimental results and comparisons are illustrated to demonstrate the effectiveness of the proposed framework.