Summarizing inter-query learning in content-based image retrieval via incremental semantic clustering

In previous work, we developed a novel relevance feedback (RF) framework that learns one-class support vector machines (ISVM) from retrieval experience to represent the set memberships of users' high level semantics. By doing a fuzzy classification of a query into the regions of support represented by the ISVMs, past experience is merged with short-term (i.e., intra-query) learning. However, this led to the representation of long-term (i.e., inter-query) learning with a constantly growing number of ISVMs in the feature space. We present an improved version of our earlier work that uses an incremental k-means algorithm to cluster ISVMs. The main advantage of the improved approach is that it is scalable and can accelerate query processing by considering only a small number of cluster representatives, rather than the entire set of accumulated ISVMs. Experimental results against real data sets demonstrate the effectiveness of the proposed method.