Shape recognition by using Scale Invariant Feature Transform for contour

This paper proposes a novel shape feature extractor named Contour-SIFT along with a matching method that computes the similarity between two set of proposed descriptors. It allows a shape to be recognized based on automatically located outstanding local features on its contour, which are extracted from 1-D signal representations of different smoothing scales. The algorithm describes each local feature as a list of frequencies from curvature histogram, which is created from curve segment around each local position. The descriptors will give high similarity compared with a model descriptors of a similar shape. The algorithm has properties of image scaling-, translation-, and rotation-invariants. An experiment were conducted with 200 images from Flavia dataset for verification. The result of using the proposed algorithm is compared with the result of using CSS.