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.

翻译：现有的基于描述性特征的物体分类技术依赖于物体对齐来计算物体间的相似性以进行分类。本文通过特征排序取代了物体对齐的必要性。物体边界经归一化处理后，被分割为近似凸片段，随后这些片段按其长度降序排列。片段长度、片段中的极值点数量、片段面积、片段基线与宽度——这一系列特征集合——被用于度量图像边界间的相似性。所提出的方法在多个数据集上进行了测试，并观察到了可接受的结果。