Investigation of Compressed Image Segmentation

This paper presents an unsupervised texture image segmentation algorithm using clustering. Two criteria are proposed in order to construct a feature space of reduced dimensions for texture image segmentation, based on selected Gabor filter set. An unsupervised clustering algorithm is applied to the reduced feature space to obtain the number of clusters, i.e. the number of texture regions [1]. A simple Euclidean distance classification scheme is used to group the pixels into corresponding texture regions. Experiments on a mixture of mosaic of textures generated by random field model show the proposed algorithm of using the clustering gives satisfactory results in terms of the number of regions and region shapes. A mathematical programming based clustering approach that is applied to a digital platform of segmentation problem involving demographic and transactional attributes related to the images. It is the collection of sub images corresponding to different image regions and scales is obtained. From the experiments, it is found that the multistage sub image matching method is an efficient way to achieve effective texture retrieval for image segmentation.