Breaking Visual Similarity Barriers: Enhanced Image Identification through Global-Local Feature Fusion

Effectively distinguishing between images in high visual similarity datasets poses significant challenges, especially with photometric variations, perspective transformations, and/or occlusions. We introduce a novel methodology that fuses local and global feature detection techniques. By integrating local feature analysis with global feature representation based on graph structuring and processing, our approach can capture topological and metric relationships among descriptors. The proposed graph representation is computed using only matching features, hence filtering irrelevant information and focusing on unique image attributes that favor identification. This study aims to answer how the synergistic combination of these techniques can outperform conventional identification methods dealing with data sets with high visual similarity. We performed experiments showing significant improvements in precision and recall, reflected in the F1-Score, of the proposed strategy over pure local-based image identification. The results highlight the potential of hybrid approaches for better image recognition, also revealing that local-based method can use our proposal as an additional component for obtaining improved results.