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Learning Contour-Based Mid-Level Representation for Shape Classification
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DOI：10.1109/ACCESS.2020.3019800.png)
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摘要
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This article proposes a novel contour-based mid-level shape description method for shape classification. This method resolves the shortcomings of low-level shape descriptors in dealing with the shapes of objects with large intra-class changes and non-linear deformation (articulation, occlusion and noise), thus improving the accuracy of shape classification. First, we extract the outer contour of an object and sample it. We next describe each sampling point on the shape contour with a triangular feature and regard it as a local feature. Then, a shape codebook is learned, and the Fisher vector encoding method is used to produce a compact mid-level shape feature. Finally, the learned mid-level shape features are sent to the linear support vector machine (SVM) classifier for shape recognition. The proposed method has been extensively tested on several standard shape datasets, and the experimental results show that our approach attains high accuracy of shape classification. Comparisons to other state-of-the-art shape classification approaches further prove the superiority and effectiveness of our method.
Keyword:
Shape
Computational modeling
Support vector machines
Data models
Strain
Two dimensional displays
Feature extraction
Shape classification
triangular feature
fisher vector
mid-level shape representation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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