Abstract
Inter-session learning in content-base image retrieval (CBIR) makes user take advantage of the information learned from previous query session. Many works have been proposed for the inter-session learning. In this thesis, the basis is a framework using a hidden semantic space to accumulate the inter-session information. At first, we use the SVM classifiers trained in short-term learning to initialize the hidden semantic space with a probabilistic model. To maintain the hidden semantic space in a proper size, we propose a novel framework based on graph-based cluster ensemble. Each time the hidden semantic space is over-expanding, we use our proposed dimension reducing method to construct a compact, effective, meaningful, and lower dimensional hidden semantic space. With the hidden semantic space, a long-term learning scheme is performed. Our experimental results demonstrate that the graph-based cluster ensemble scheme works well and efficient in our long-term learning CBIR system. The scheme takes short time but provides stable and reliable results.