2DIGH: a polar invariant local image descriptor based on joint histogram

Abstract

One of the key challenges of current image matching techniques is how to build a robust local descriptor which is invariant to large variations in scale and rotation. To address this issue, in this work a polar gradient local oriented histogram pattern (PGP) is localized on normalized cropped regions around detected interest points. Then, a new image descriptor named two-dimensional intensity gradient histogram (2DIGH) is introduced using the joint histogram scheme. 2DIGH builds the extracted feature vector by intersecting of gradient and intensity information on subregions of the PGP. The measured distance with K-nearest neighbor represents feature vectors similarity/distance for image matching. The experimental results on Graffiti, Boat, Bark and ZuBud datasets indicate that the performance of the introduced 2DIGH is at least 41% better than other widely applied descriptors.