Shape-based image retrieval for thematic database

Large image databases are used in many multimedia applications such as entertainment, business, art, engineering and science. Searching information in these databases is a crucial problem to be solved for the development of visual information system. Therefore efficient retrieval methods are required for the purpose of finding a desired image from a collection of images. This research is focused on developing a shape-based image retrieval system for use in thematic databases. For this purpose, a novel shape similarity matching methodology is needed in order to detect a small similarity difference between two images. In this paper, a new shape transformation approach, referred to as NURBS-warping approach is implemented. In the proposed approach, non uniform rational B-spline (NURBS) and gradient vector flow (GVF) are incorporated to ensure accurate and fast retrieval results. NURBS is a compact and accurate shape descriptor, whereas GVF ensure fast and accurate matching results. The effectiveness of this approach is examined by carrying out experiments on a collection of 1100 similar images from a thematic fish database. The overall retrieval results show that the proposed approach is able to derive an accurate similarity measure that resembles human similarity judgement.