Robust template matching algorithm with multi-feature using best-buddies similarity

Abstract

In order to solve the problem of matching failure of BBS (Best-Buddies Similarity) algorithm when the target image has a partial occlusion, cluttered background, imbalance illumination, and nonrigid deformation. A multi-feature template matching algorithm based on the BBS algorithm is proposed in this paper. On the basis of the location features and appearance features, we add HOG (Histogram of Oriented Gradients) features to make full use of the color, position and structural contour of the target image to match. In addition, we also perform mean filtering on the confidence map. The experimental results show that the AUC (Area Under Curve) score of the proposed algorithm is 0.6119, which is 6.38% higher than the BBS algorithm. Moreover, our algorithm has stronger robustness and higher matching accuracy.