Content-based image retrieval system for multi-object images using a combination of features

Abstract

Content-based image retrieval (CBIR) is a research area dedicated to address the retrieval of images based on automatically derived features from the content of the images in database. Traditional CBIR systems generally compute global features of the image for example, based on color histograms. When a query images is fired, it returns all those images whose features match closely with the query image. The major disadvantage of such systems based on global features is that they return the images that match globally but cannot possibly return images corresponding to some particular objects in the query image. The thesis addresses this problem and proposes a CBIR system for multi object image database with 3D objects using the properties of the object in the images for retrieval. Object segmentation has been achieved using GVF Active Contour. An inherent problem with active contours is initialization of contour points. The thesis proposes an approach for automatic initialization of contour points. Experimental results show that the proposed approach works efficiently for contour initialization. In the thesis in addition to shape feature using modified chain code other features for object retrieval using colour with the aid of colour moments and texture using Gabor Wavelets have also been used. A comparative study has been made as to which combination of features performs better. Experimental results indicate that the combination of shape and color feature is a strong feature for image retrieval.