| Large image databases are becoming popular due to the ease with which images are being created/digitized and stored. Content Based Image Retrieval (CBIR) has therefore evolved into a necessity. It is a challenging task to design an effective and efficient CBIR system. Current research works attempt to obtain the semantics or meaning of the image to perform better retrieval. Segmentation of an image into regions may reveal the true objects in the image. The local properties of regions can help matching objects between images and thereby contribute towards a more meaningful CBIR.; The main contribution of this thesis is a CBIR algorithm, called SNL, that utilizes the regional properties of the images. Each image is segmented and features including the colour, shape, size and spatial position of the region are extracted. Regions are matched by comparing the region content, shape and spatial position and the Integrated Region Matching (IRM ) distance measure between the whole images is calculated. The relative importance of the above features is investigated. SNL out-performs the Global Colour Histograms (GCH) and Colour Based Clustering (CBC) in terms of precision-recall.; A more efficient version of SNL, SNL +, is designed using the Omni filtering technique recently proposed, along with the IRM distance measure. Our experiments have shown that SNL+ can significantly reduce the query time without losing effectiveness. |
