A novel local invariant feature detection and description algorithm

Local invariant features have been successfully applied in many applications in computer vision. This paper proposes a novel local feature detection and description algorithm. The features are invariant to image rotation, scale and illumination changes, and even can be invariant to weak affine transformations. In general, the local feature extraction process can be divided into two key steps which are feature detection step and feature description step. In the detection step, firstly, the Harris corners are detected in every scale level image. Secondly, the local scale-space extrema is searched within a window which is center-localized on the multi-scale Harris corners. Finally, the predominant orientation is computed for each keypoint. The proposed feature detection algorithm has good repeatability performance. In the description step, a novel local descriptor is created based on the gradient distance and orientation histogram (GDOH). GDOH not only has good matching performance, but also has low dimensionality, which results in much faster feature matching speed. Extensive experimental results have demonstrated the effectiveness and efficiency of the proposed algorithm.