Appling grey relational analysis to the relevance feedback in content-based image retrieval

Based on the quantitative grey relational analysis method, a simple and effective user query learning algorithm for the relevance feedback in content-based image retrieval is proposed. This new approach is an supervised algorithm, and the query parameters can be dynamically updated via relevance feedback to reflect the user’s particular information need. Experimental results shows that the proposed method performs better than the previous GRA-based algorithms for learning the query parameters in the learning precision and the generalization ability, and thus the performance of the relevance feedback for content-based image retrieval can be considerably improved.