Traditional Euclidean distance based complex network is usually sensitive to the non-rigid transformation of the shape image. To overcome this problem

in this paper

a novel shape feature extraction Method based on complex network model and relative coherent distance is proposed. First

an initial complex network is constructed with nodes corresponding to the boundary points and edges allocated with relative coherent distance as weights existing between each node pairs. Then

this initial network is threshold evolved to generate a series of sub-networks. At last

some topological features are extracted from these sub-networks to generate the feature descriptors for the shape image. Promising experimental Results on classification and retrieval show that the proposed Method has strong capability in discriminating and recognizing variety of object shapes. Comparing with the traditional distances

the relative coherent distance is more robust to the shape non-rigid and elastic transformations.