Template matching in the wild with weighted assembled similarity

In this paper, we propose a template matching algorithm that is robust to deformations and background clutters. A weighted assembled similarity measure is constructed to discover the similarity between two different distributions, and a two-step nearest neighbor searching algorithm is designed to provide the feature points with different weights, which makes it more distinctive when calculating the similarity between the candidate image and the template. A local feature descriptor named Progressive Gradient Descriptor is also put forward to encode the input image to a high-dimensional feature map. Experiments on real-scene data prove that the proposed algorithm is competitive in terms of matching accuracy.