Interwoven texture-based description of interest points in images

Abstract

Local feature description is to assign a unique signature to a key-point such that it becomes distinctive from the others regardless of changes in viewpoint, illumination, rotation, scale as well as distortions and noise. This paper proposes a novel approach to construct such a descriptor. For preserving both homogeneous and heterogeneous features of a given support region, we interweave the texture information so that the key-point is more likely to be assigned a distinctive signature and neighboring key-points will be less likely to share the same texture information. The main idea behind our descriptor is to increase the areas of our observations in the given scene while the length of the local support region is fixed. Gradient magnitude and divergence, as measurement parameters of texture information, are applied to a group of pixels instead of employing a pixel-wise strategy that make the descriptor more resistant to noise, distortions and illumination variation. The required storage of the proposed descriptor is just 72 floats and its computational complexity is much lower than those of existing ones. A comparative study between the proposed method and the selected state-of-the-art ones over multiple publicly accessible datasets with different characteristics shows its superiority, robustness and computational efficiency under various geometric changes, illumination variation, distortions and noise. The code and supplementary materials can be found at https://github.com/mogvision/InterTex-Feature-Descriptor.