LISF: An Invariant Local Shape Features Descriptor Robust to Occlusion

In this work an invariant shape features extraction, description and matching method (LISF) for binary images is proposed. In order to balance the discriminative power and the robustness to noise and occlusion in the contour, local features are extracted from contour to describe shape, which are later matched globally. The proposed extraction, description and matching methods are invariant to rotation, translation, and scale and present certain robustness to partial occlusion. Its invariability and robustness are validated by the performed experiments in shape retrieval and classification tasks. Experiments were carried out in the Shape99, Shape216, and MPEG-7 datasets, where different artifacts were artificially added to obtain partial occlusion as high as 60%. For the highest occlusion levels the proposed method outperformed other popular shape description methods, with about 20% higher bull’s eye score and 25% higher accuracy in classification.