Learning a complex network representation for shape classification

Author(s)

Ribas, Lucas C. ; Bruno, Odemir M.

• Document type: Journal article
• Source: PATTERN RECOGNITION; v. 154, p. 10-pg., 2024-05-18.

Abstract

Shape contour is a key low-level characteristic, making shape description an important aspect in many computer vision problems, with several challenges such as variations in scale, rotation, and noise. In this paper, we introduce an approach for shape analysis and classification from binary images based on representations learned by applying Randomized Neural Networks (RNNs) on feature maps derived from a Complex Network (CN) framework. Our approach models the contour in a complex network and computes their topological measures using a dynamic evolution strategy. This evolution of the CN provides significant information into the physical aspects of the shape's contour. Therefore, we propose embedding the topological measures computed from the dynamics of the CN evolution into a matrix representation, which we have named the Topological Feature Map (TFM). Then, we employ the RNN to learn representations from the TFM through a sliding window strategy. The proposed representation is formed by the learned weights between the hidden and output layers of the RNN. Our experimental results show performance improvements in shape classification using the proposed method across two generic shape datasets. We also applied our approach to the recognition of plant leaves, achieving high performance in this challenging task. Furthermore, the proposed approach has demonstrated robustness to noise and invariance to transformations in scale and orientation of the shapes. (AU)

• FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
• Grantee: Lucas Correia Ribas
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 23/04583-2 - Pattern recognition in images based on artificial neural networks and complex systems: from handcrafted descriptor extraction to automated learning
• Grantee: Lucas Correia Ribas
• Support Opportunities: Regular Research Grants
• FAPESP's process: 21/07289-2 - Learning Representations using artificial neural networks and complex networks with applications in sensors and biosensors
• Grantee: Lucas Correia Ribas
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants