Sketch-based image retrieval using contour segments

The paper presents a sketch-based image retrieval algorithm. One of the main challenges in sketch-based image retrieval (SBIR) is to measure the similarity between a sketch and an image in contour with high precision. To tackle this problem, we divided the contour of image into two types: the first is global contour, suggesting that we can use it to reduce the similarity between the images with complex background. The second, called salient contour, is helpful to retrieve images with objects similar to the query. Besides, we propose a new descriptor, namely angular radial orientation partitioning (AROP) feature, which makes full use of the gradient orientation information to decrease the gap between sketch and image. Using the two contours as candidate contours for feature extraction could increase the retrieval rate dramatically. Finally an application of retrieval system based on this algorithm is established. The experiment on 0.42 million image dataset shows excellent retrieval performance of the proposed method and comparisons with other algorithms are also given.