A parameter adjustment method for relevance feedback

Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By using relevance feedback, CBIR allows the user to progressively refine the system's response to a query. In this paper, after analyzing the feature distributions of positive and negative feedbacks, a new parameter adjustment method for iteratively improving the query vector and adjusting the weights is proposed. Experimental results demonstrate the effectiveness of this method.