A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques

Abstract

Keypoint descriptor plays a significant role in a huge number of computer vision applications. A large amount of effort and a number of techniques are proposed in the literature which tried to build an image patch descriptor in different binary and n-n-binary spaces. Despite considerable performance of some existing techniques, there are still open problems to be resolved such as lack of enough reliability and robustness against some image distortions and transformations, especially brightness change, blur and JPEG compression. To address these issues, a keypoint descriptor which is adapted from Tomographic Image Reconstruction is proposed in this research. Convolution of predefined Gaussian smoothed sensitivity maps and associated image patch produce a matrix whose entities indicate the average intensity of the pixels at the convolved pixels in the image patch. The initial descriptor is constructed by finding the absolute differences of all possible pairs of matrix. Genetic Algorithm (GA) and Principal Component Analysis (PCA) are used to optimize this descriptor vector to its most discriminative features. Experimental result shows that the proposed descriptor outperformed some existing techniques particularly in brightness change, JPEG compression and blur while it has reasonable performance in other transformations.