Visual Semantic Complex Network for Web Images

Shi Qiu, Xiaogang Wang, Xiaoou Tang; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 3623-3630

This paper proposes modeling the complex web image collections with an automatically generated graph structure called visual semantic complex network (VSCN). The nodes on this complex network are clusters of images with both visual and semantic consistency, called semantic concepts. These nodes are connected based on the visual and semantic correlations. Our VSCN with 33, 240 concepts is generated from a collection of Nwwmillion web images. iiA great deal of valuable information on the structures of the web image collections can be revealed by exploring the VSCN, such as the small-world behavior, concept community, indegree distribution, hubs, and isolated concepts. It not only helps us better understand the web image collections at a macroscopic level, but also has many important practical applications. This paper presents two application examples: content-based image retrieval and image browsing. Experimental results show that the VSCN leads to significant improvement on both the precision of image retrieval (over 200%) and user experience for image browsing.