Clustering of Digital Images using Shape Features with SOM

Abstract

In these days people are interested in using digital images. So the size of image databases is increasing rapidly. It leads retrieval problem of images from large databases. Machine learning algorithms are applying in recent research to simplify the task of image retrieval and make it automatic. Thus the concept of content based image retrieval system came into existence. In this system the images are extracted based on similar content. Content means features of the images and it is formed by feature extraction of the images in databases. Contents can be edges, color, shape, gradient, orientation, histogram gradient etc. These contents are clustered into various groups of similar feature vectors. So for any input image the selected feature is searched for and image is retrieved from the database. This reduces the time complexity. There have been many algorithms for implementing the content based image retrieval system. In this research work we propose a novel paradigm where in shape features are extracted from the database images and are used to train the self-organizing map to cluster the shape features. These clusters are then used for image retrieval.