Atnafu, Solomon(PhD)Gashaw, Estifanos2018-06-192023-11-042018-06-192023-11-042004-06http://etd.aau.edu.et/handle/123456789/1794The availability of images in electronic format has resulted in large number of digital images being used for different applications. The growth in the usage and availability of digital images calls for an efficient means of image data management. This has led to the development of content based image retrieval (CBIR) systems, which uses visual contents of an image such as color, texture or shape for querying image databases. CBIR is well researched and there are a large number of image retrieval systems based on this technology. Image retrieval matching can be performed using global features of the whole image (as in CBIR), but a comparison that considers part(s) of images for similarity-based retrieval is sometimes a more natural approach. Regions of an image that are of particular interest to humans are termed salient-objects of an image. Salient –object based image retrieval is not well researched, but there some developed system that model the salient object of the image using a Minimum Bounding Rectangle. In this work a new approach is proposed which considers the natural shape of the salient objects contained within an image. An Image segmentation tool is used to automatically identify salient objects of an image and store them in an object relational DBMS for multi-criteria image query (query based on both image content, positional predicates and metadata). Furthermore, some techniques have been proposed in this work to make the image retrieval system accessible within a pervasive computing environment. Keywords: Salient-Object Based Image Retrieval, Image Database, Image Retrieval in Pervasive Environment, Image Segmentation, Multi-Criteria Image Query.enSalient-Object Based Image Retrieval, Image Database, Image Retrieval in Pervasive Environment; Image Segmentation; Multi-Criteria Image QueryThesis