Visual graphs for image classification: does the structure affect performance?

2019-11-10 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Computer Vision · Depth: Expert, extended

Summary

This work addresses a gap by systematically comparing current graph construction techniques within a fixed three-layer Graph Convolutional Network (GCN) architecture for image classification. Through an empirical study on the Fashion-MNIST dataset (70,000, 28x28 pixel grayscale images), it demonstrates how network structure significantly affects performance. The study fixed the maximum number of nodes at 50 (approximately 6.4% of image pixels) and evaluated three node extractors (regular grid 7x7, superpixel SLIC, interest point Harris), three feature embeddings (ViT-based 768-d, CNN-based ResNet18 256-d, SIFT-like 130-d), and three edge sparsification methods (average distance, Locality Sensitive Pruning, Minimum Spanning Tree). The best overall result was achieved with interest points, ViT features, and the MinCONN filter, yielding a test accuracy of 0.8681 and an F1 score of 0.8672. ViT features consistently outperformed CNN and SIFT features by 1.4% and 4.0% respectively, and sparse, carefully pruned graph topologies generally led to better performance.

Key takeaway

For Machine Learning Engineers designing GNNs for image classification, your graph architecture choices are critical. Prioritize using richer features like ViT-based embeddings, as they enable more effective, sparser graphs. Implement graph sparsification techniques, especially Minimum Spanning Tree (MinCONN), to achieve high accuracy with reduced complexity. This approach can lead to better performance and more efficient models, even on resource-constrained deployments.

Key insights

Graph architecture, particularly sparsification, significantly modulates the utility of node features for image classification.

Principles

• Richer node features enable sparser, more efficient graphs.
• Graph sparsification improves GNN performance.
• Structural quality outweighs connectivity quantity.

Method

A systematic comparison of graph construction techniques for GCNs, evaluating node extraction (grid, superpixel, interest point), feature embeddings (ViT, CNN, SIFT), and edge sparsification (average distance, LSP, MinCONN) on image classification.

In practice

• Prioritize ViT-based features for GNN nodes.
• Apply Minimum Spanning Tree (MinCONN) for edge pruning.
• Limit nodes to ~6.4% of image pixels (e.g., 50 for 28x28).

Topics

• Graph Neural Networks
• Image Classification
• Graph Construction
• Graph Sparsification
• Vision Transformers
• Feature Extraction

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.