Article ID Journal Published Year Pages File Type
530215 Pattern Recognition 2015 13 Pages PDF
Abstract

•A shape classifier tolerant to scale, rotation and viewpoint changes is proposed.•The inclusion of the histogram of bi-grams improves the bag-of-words model.•Codebook selection and feature learning improve the classification accuracy.•We report best results on the animal shapes dataset using the proposed method.

In this paper, we describe a classification framework for binary shapes that have scale, rotation and strong viewpoint variations. To this end, we develop several novel techniques. First, we employ the spectral magnitude of log-polar transform as a local feature in the bag-of-words model. Second, we incorporate contextual information in the bag-of-words model using a novel method to extract bi-grams from the spatial co-occurrence matrix. Third, a novel metric termed ‘weighted gain ratio’ is proposed to select a suitable codebook size in the bag-of-words model. The proposed metric is generic, and hence it can be used for any clustering quality evaluation task. Fourth, a joint learning framework is proposed to learn features in a data-driven manner, and thus avoid manual fine-tuning of the model parameters. We test our shape classification system on the animal shapes dataset and significantly outperform state-of-the-art methods in the literature.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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