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

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 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 re-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 user preference for Agate. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – retrieval models, clustering. G.3 [Mathematics of Computing]: Probability and Statistics – nonparametric statistics. H.5.3 [Infor...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Yuheng Ren, Mo Yu, Xin-Jing Wang, Lei Zhang, Wei-Ying Ma
• Keywords: Agate, Internet Technology, Landmark Image Search, Landmark Photo Search, WWW 2010