Colour image indexing using SOM for region-of-interest retrieval
In this paper, we present a novel approach to image indexing by incorporating a neural network model, Kohonen's Self-Organising Map (SOM), for content-based image retrieval. The motivation seems from the idea of finding images by regarding users specifications or requirements imposed on the query, which has been ignored in most existing image retrieval systems. An important and unique aspect of our interactive scheme is to allow the user to select a Region-Of-Interest (ROI) from the sample image, and subsequent query concentrates on matching the regional colour features to find images containing similar regions as indicated by the user. The SOM algorithm is capable of adaptively partitioning each image into several homogeneous regions for representing and indexing the image. This is achieved by unsupervised clustering and classification of pixel-level features, called Local Neighbourhood Histograms (LNH), without a priori knowledge about the data distribution in the feature space. The indexes generated from the resultant prototypes of SOM learning demonstrate fairly good performance over an experimental image database, and therefore suggest the effectiveness and significant potential of our proposed indexing and retrieval strategy for application to content based image retrieval.
image indexing region-of-interest local neighbourhood histogram content-based retrieval self organising map