Shape-matching approach to content-based image retrieval

In this paper, to improve the retrieval effectiveness of a content-based image retrieval system, a shape-based object matching method is presented. A new skeleton structure is proposed as a shape representation. The skeleton structure represents an object in a hierarchical manner such that high-level nodes describe parts of coarse trunk of the object and low-level nodes describe fine details. Each low- level node refines the shape of the parent node. Most of the noise disturbances are limited to the bottom levels. The effect of boundary noise is reduce by decreasing weights on the bottom levels. To compute the similarity of two skeleton structures, we consider the best match of spine nodes, nodes in level one of the structure. Both moment invariants and Fourier descriptors are used to compute the similarities of sub-regions. We evaluated the retrieval accuracy and compared the result to that of other shape similarity measures. Experimental results showed that our system gives prominent accuracy in retrieval.