Quadrant-based contour features for accelerated shape retrieval system

Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.