An Efficient Shape Feature Extraction, Description and Matching Method Using GPU

Abstract

Shape information is an important cue for many computer vision applications. In this work we propose an invariant shape feature extraction, description and matching method for binary images, named LISF. The proposed method extracts local features from the contour to describe shape and these features are later matched globally. Combining local features with global matching allows us to a obtaining a trade-off between discriminative power and robustness to noise and occlusion in the contour. The proposed extraction, description and matching methods are invariant to rotation, translation, and scale and present certain robustness to partial occlusion. The conducted experiments in the Shapes99, Shapes216, and MPEG-7 datasets support the mentioned contributions, where different artifacts were artificially added to obtain partial occlusion as high as 60 %. For the highest occlusion levels LISF outperformed other popular shape description methods, with about 20 % higher bull’s eye score and 25 % higher accuracy in classification. Also, in this paper, we present a massively parallel implementation in CUDA of the two most time-consuming stages of LISF, i.e., the feature extraction and feature matching steps; which achieves speed-ups of up to 32x and 34x, respectively.