Data mining refers to the process of extracting knowledge that is of interest to the user. Traditional data mining techniques have been developed mainly for structured data types. The image data type does not belong to this structured category, suitable for interpretation by a machine and hence the mining of image data is a challenging problem. Accordingly, in image mining, an image retrieval system is a computer system that can browse, search and retrieve images from a large database of digital images. This research work is aimed at compression and retrieval of images from large image archives. A Kohonen Self Organization Map approach using content categorization, including feature level clustering, is developed to provide a differential compression scheme. It ensures that the visual features are mapped to codebooks, which significantly speed up content-based retrieval. The interaction between compression and content indexing are proposed, which include techniques for feature extraction, indexing, and categorization. K-means clustering algorithm is used to build the feature cluster. This approach leads to the similarity matching based on shape and texture, which supports functions like "query by example". Experimental results demonstrate that the proposed method can improve the compression ratio compared to VQ. The average retrieval time is less than 2seconds, which is proved to be efficient.
