A Short-Term and Long-Term Learning Approach for Content-Based Image Retrieval

Abstract

This paper proposes a short-term and long-term learning approach for content-based image retrieval. The proposed system integrates the user's positive and negative feedback from all iterations to construct a semantic space to remember the user's intent in terms of the high-level hidden semantic features. The short-term learning further refines the query by updating its associated weight vector using both positive and negative examples together with the long-term-learning-based semantic space. The similarity score is computed as the dot product between the query weight vector and the high-level features of each image stored in the semantic space. Our proposed retrieval approach demonstrates a promising retrieval performance for an image database of 6000 general-purpose images from COREL, as compared with the conventional retrieval systems.