Please use this identifier to cite or link to this item: https://doi.org/10.1145/2009916.2010078
Title: Optimizing multimodal reranking for web image search
Authors: Li, H.
Wang, M. 
Li, Z.
Zha, Z.-J. 
Shen, J.
Keywords: Graph-based learning
Image search
Reranking
Issue Date: 2011
Source: Li, H.,Wang, M.,Li, Z.,Zha, Z.-J.,Shen, J. (2011). Optimizing multimodal reranking for web image search. SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval : 1119-1120. ScholarBank@NUS Repository. https://doi.org/10.1145/2009916.2010078
Abstract: In this poster, we introduce a web image search reranking approach with exploring multiple modalities. Different from the conventional methods that build graph with one feature set for reranking, our approach integrates multiple feature sets that describe visual content from different aspects. We simultaneously integrate the learning of relevance scores, the weighting of different feature sets, the distance metric and the scaling for each feature set into a unified scheme. Experimental results on a large data set that contains more than 1,100 queries and 1 million images demonstrate the effectiveness of our approach.
Source Title: SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/40102
ISBN: 9781450309349
DOI: 10.1145/2009916.2010078
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 11, 2017

Page view(s)

66
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.