Shape Classification using Approximately Convex Segment Features

TL;DR

This paper introduces a shape classification method that uses approximately convex segment features and boundary normalization to compare objects without requiring alignment, achieving promising results on datasets.

Contribution

It presents a novel boundary segmentation and feature sorting approach that eliminates the need for object alignment in shape classification.

Findings

Effective shape classification without alignment.

Uses boundary segmentation and feature sorting.

Achieved acceptable results on datasets.

Abstract

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.