Improving image retrieval performance by integrating long-term learning with short-term learning

Content-based image retrieval has become an active research topic for more than one decade. Nevertheless, current image retrieval systems still have major difficulties bridging the gap between the user's implied concept and the low-level image description. To address the difficulties, this paper presents a novel image retrieval model integrating long-term learning with short-term learning. This model constructs a semantic image link network by long-term learning which simply accumulates previous users' relevance feedback. Then, the semantic information learned from long-term learning process guides short-term learning of a new user. The image retrieval is based on a seamless joint of both long-term learning and short-term learning. The model is easy to implement and can be efficiently applied to a practical image retrieval system. Experimental results on 10,000 images demonstrate that the proposed model is promising.