Vector image segmentation for content-based vector image retrieval

This paper proposes a novel method for vector image segmentation as the first step in developing a content-based vector image retrieval system. Structure of a vector image can be represented as a tree in which each node is assigned each object region in the vector image and each link represents the inclusion relation between two object regions. In order to generate such trees, the method separates object regions from background by detecting figures defining boundary between object regions and background. The proposed method finds object regions before rasterizing, which is an essential difference from existing object separation methods. We have evaluated the effectiveness of the proposed object separation on 40 test vector images by comparing manual object separation. The experimental results have shown that the proposed method has a high performance which is comparable to manual object separation.