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

Authors:
Yuheng Ren;Mo Yu;Xin-Jing Wang;Lei Zhang;Wei-Ying Ma
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China
Venue:
Proceedings of the 19th international conference on World wide web
Year:
2010

Citing 4
Cited 3

Modeling annotated data

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Grouping web image search result

Proceedings of the 12th annual ACM international conference on Multimedia
IGroup: web image search results clustering

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Evaluating Color Descriptors for Object and Scene Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Research and applications on georeferenced multimedia: a survey

Multimedia Tools and Applications
DLMSearch: diversified landmark search by photo

Proceedings of the 20th ACM international conference on Multimedia
Searching for diversified landmarks by photo

Proceedings of the 20th ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

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.