Diversifying landmark image search results by learning interested views from community photos

Yuheng Ren, Mo Yu, Xin-Jing Wang, Lei Zhang, Wei-Ying Ma

Abstract: In this paper, we demonstrate a novel landmark photo search and browsing system: Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate the user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and set-based ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and users' preference for Agate.