Shape-based image retrieval using two-level similarity measures

In this paper, we present a novel method of using two-level similarity measures for shape-based image retrieval. We first identify the dominant points of a given shape, and then calculate their geometric moments and the distances between two consecutive dominant points. A spectrum representing the normalized geometric moments versus normalized distances is generated, and its area and curve length are computed. We use these two values as similarity features for the indexes in coarse-grained shape retrieval. Furthermore, we use the cross-sectional area and curve length distribution for the indexes in fine-grained shape retrieval. Experimental results show that the proposed method is simple and efficient and can reach the accuracy rate of 95%.