Multi-Layer Local Graph Words for Object Recognition

TL;DR

This paper introduces a multi-layer graph-based feature extraction method for object recognition, which improves performance over traditional Bag-of-Visual-Words by capturing structural information at multiple scales.

Contribution

It presents a novel multi-layer local graph approach using Delaunay triangulation and Bag-of-Graph-Words, enhancing object recognition accuracy.

Findings

Multi-layer graph features outperform single-layer methods.

Combining layers yields significant accuracy improvements.

Graph features are complementary across different layers.

Abstract

In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local features. The structural features we propose are nested multi-layered local graphs built upon sets of SURF feature points with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth to a Bag-of-Graph-Words representation. The multi-layer nature of the descriptors consists in scaling from trivial Delaunay graphs - isolated feature points - by increasing the number of nodes layer by layer up to graphs with maximal number of nodes. For each layer of graphs its own visual dictionary is built. The experiments conducted on the SIVAL and Caltech-101 data sets reveal that the graph features at different layers exhibit complementary performances on the same content and perform better than baseline BoVW approach. The combination of all existing layers, yields significant improvement of the object recognition performance compared to single level approaches.