抽象的

Cluster Based Image Retrieval

Dr.A.Muthu Kumaravel

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 is known as the semantic gap. We introduce a novel image retrieval scheme CLUster-based rEtrieval of images by unsupervised learning which tackles the semantic gap problem based on a hypothesis: semantically images tend to be clustered in some feature space. 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; clusters give the algorithm as well as the user’s semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure. Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60,000 images from COREL demonstrate improved performance.