Abstract
In this work, we study efficiency of SIFT descriptor in discrimination of binary shapes. We also analyze how the use of \(2-tuples\) of SIFT keypoints can affect discrimination of shapes. The study is divided into two parts, the first part serves as a primary analysis where we propose to compute overlap of classes using SIFT and a majority vote of keypoints. In the second part, we analyze both classification and matching of binary shapes using SIFT and Bag of Features. Our empirical study shows that SIFT although being considered as a texture feature, can be used to distinguish shapes in binary images and can be applied to the classification of foreground’s silhouettes.
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Setitra, I., Larabi, S. (2015). SIFT Descriptor for Binary Shape Discrimination, Classification and Matching. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_41
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DOI: https://doi.org/10.1007/978-3-319-23192-1_41
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