不依赖对齐，用凸段长度排序实现形状分类
Shape Classification using Approximately Convex Segment Features
- 通过排序边界凸段长度替代物体对齐
- 在多个数据集上取得可接受的分类效果
- 适合无对齐约束的形状识别任务
基于描述性特征的现有物体分类方法依赖物体对齐来计算相似性。本文通过特征排序消除了对物体对齐的需求：将物体边界归一化并分割为近似凸段，按段长降序排列；利用段长、极点数、面积、底边与宽度等构成特征袋，衡量图像边界的相似性。该方法在多个数据集上测试，取得了可接受的分类结果。
原文摘要 · Abstract (English)
The existing object classification techniques based on descriptive features rely on object alignment to compute the similarity of objects for classification. This paper replaces the necessity of object alignment through sorting of feature. The object boundary is normalized and segmented into approximately convex segments and the segments are then sorted in descending order of their length. The segment length, number of extreme points in segments, area of segments, the base and the width of the segments - a bag of features - is used to measure the similarity between image boundaries. The proposed method is tested on datasets and acceptable results are observed.
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