This page holds information about the CLUE project, as well as other work related to machine learning and region-based image retrieval, conducted by Yixin Chen. These include the UFM matching technique, image categorization by learning and reasoning with regions, and the MILES work.
The project was conducted by Yixin Chen (now on faculty of University of Mississippi) and James Z. Wang of The Pennsylvania State University.
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This discrepancy between low-level features and high-level concepts is known as the semantic gap. This project introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which attempts to tackle the semantic gap problem based on a hypothesis that images of the same semantics are similar in a way, images of different semantics are different in their own ways. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. We acknowledge NSF for the funding and equipment support.