Graph-Density-Based Visual Word Vocabulary For Image Retrieval

Descriptive visual word vocabulary serves as the foundation of large scale image retrieval systems. However, the visual word descriptive power is limited by the construction mechanisms based on either cluster center or partitioned feature space, since such mechanisms may merge the sparsely distributed features and split the densely distributed features. Besides, there are a large number of outlier features that are not similar with any visual word. Quantizing such features into visual words inevitably decreases the visual word descriptive power. In this paper, we propose a novel Graph-Density-based visual word Vocabulary (GDV), which constructs the visual word by dense feature subgraph and directly measures the intra-word similarity by the corresponding graph density. Our method remarkably enhances the visual word descriptive power from the following three aspects: 1) GDV guarantees the high intra-word similarity by constructing visual words under the criterion of large graph density; 2) GDV improves the inter-word dissimilarity by alleviating the unexpected effect of subgraph splitting; 3) GDV suppresses the influence of outlier features by selectively quantizing only the features that are similar enough with the visual words. Extensive experiments demonstrate GDV's advanced descriptive power over traditional visual word vocabularies in enhancing both the retrieval accuracy and efficiency, which provides a higher level starting point for most image retrieval systems.