Shape Classification using Approximately Convex Segment Features

2026-01-07

arXiv.org

AI Summary

This work proposes a novel alignment-free shape representation for object classification that overcomes the limitations of traditional descriptor-based methods, which rely on object alignment to compute shape similarity and thus suffer from reduced efficiency and generalization. The approach begins by normalizing object contours, decomposing them into approximately convex segments, and ordering these segments by descending length. A multidimensional geometric feature vector—encompassing segment length, number of extremal points, area, base length, and width—is then extracted to construct a structure-aware representation for similarity measurement and classification. By eliminating the need for explicit alignment, the method achieves competitive classification accuracy across multiple datasets while significantly enhancing the robustness and scalability of shape comparison.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• shape classification
• object alignment
• convex segments
• feature sorting
• boundary similarity

Innovation

• approximately convex segments
• alignment-free classification
• boundary segmentation
• bag of features
• shape classification