Beyond 2D-grids: a dependence maximization view on image browsing
Novi Quadrianto, Kristian Kersting, Tinne Tuytelaars and Wray Buntine
In: Proceedings of the international conference on Multimedia information retrieval, ACM, New York, NY(2010).
Ideally, one would like to perform image search using an intuitive and friendly approach. Many existing search engines return a set of pages, where each page contains several images ranked based on how relevant they are to the query. While this certainly has its advantages, arguably, a more intuitive way to present the image search result is to organize images based on their similarity, for example, according to color or semantics, into arbitrary structures such as hierarchies and spheres. This paper focuses on designing such a navigation system for image browsers. The design uses a recently developed machine learning technique, called kernelized sorting. Kernelized sorting is a general technique for matching pairs of objects from dierent domains without requiring the cross-domain similarity measure and hence allows sorting images into arbitrary structures. Moreover, some images can be preselected for instance forming the tip of the hierarchy allowing to subsequently navigate through the search results in the lower levels in an intuitive way.