Efficient Tree-Based Feature Descriptor and Matching Algorithm

Abstract

This paper presents the development of a new feature descriptor derived from previous work on the basis sparse-coding inspired similarity descriptor that provides smaller descriptor size, simpler computations, faster matching speed, and higher accuracy. The TreeBASIS descriptor algorithm uses a binary vocabulary tree that is computed offline using basis sparse-coding inspired similarity dictionary images derived from sparse coding and a test set of feature region images. The resulting tree is stored in memory for online high-speed searching for feature matching. During the online matching stage, a feature region image is binary quantized and the resulting quantized vector is passed into the basis sparse-coding inspired similarity tree. A Hamming distance is computed between the feature region images and the effectively descriptive basis sparse-coding inspired similarity dictionary images at the current node to determine the branch taken. The path the feature region image takes is saved as the descriptor, and matching is performed by following the paths of two features and iteratively reducing the distance as the path is traversed. Experimental results show that the TreeBASIS descriptor is better than basis sparse-coding inspired similarity, scale invariant feature transform, and speeded-up robust features on frame-to-frame aerial feature point matching. It requires much less computational resources and runs faster.