Paper Info
Title
T-IRS: textual query based image retrieval system for consumer photos
Abstract
In this demonstration, we present a (quasi) real-time textual query based image retrieval system (T-IRS) for consumer photos by leveraging millions of web images and their associated rich textual descriptions (captions, categories, etc.). After a user provides a textual query (e.g., "boat"), our system automatically finds the positive web images that are related to the textual query "boat" as well as the negative web images which are irrelevant to the textual query. Based on these automatically retrieved positive and negative web images, we employ the decision stump ensemble classifier to rank personal consumer photos. To further improve the photo retrieval performance, we also develop a novel relevance feedback method, referred to as Cross-Domain Regularized Regression (CDRR), which effectively utilizes both the web images and the consumer images. Our system is inherently not limited by any predefined lexicon.
Year
DOI
Venue
2009
10.1145/1631272.1631479
ACM Multimedia 2001
Keywords
Field
DocType
personal consumer photo,consumer photo,textual query,consumer image,associated rich textual description,web image,negative web image,positive web image,image retrieval system,real-time textual query,image retrieval,real time
Web search query,Relevance feedback,Query expansion,Information retrieval,Computer science,Web query classification,Image retrieval,Classifier (linguistics),Multimedia,Decision stump,Visual Word
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Yiming Liu125125.55
Dong Xu27616291.96
Ivor W. Tsang35396248.44
Jiebo Luo46314374.00