NDLI: Weak attributes for large-scale image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Yu, F.X. Rongrong Ji Ming-Hen Tsai Guangnan Ye Shih-Fu Chang
Copyright Year	2012
Description	Author affiliation: Columbia University, New York, NY 10027 (Yu, F.X.; Rongrong Ji; Ming-Hen Tsai; Guangnan Ye; Shih-Fu Chang)
Abstract	Attribute-based query offers an intuitive way of image retrieval, in which users can describe the intended search targets with understandable attributes. In this paper, we develop a general and powerful framework to solve this problem by leveraging a large pool of weak attributes comprised of automatic classifier scores or other mid-level representations that can be easily acquired with little or no human labor. We extend the existing retrieval model of modeling dependency within query attributes to modeling dependency of query attributes on a large pool of weak attributes, which is more expressive and scalable. To efficiently learn such a large dependency model without overfitting, we further propose a semi-supervised graphical model to map each multiattribute query to a subset of weak attributes. Through extensive experiments over several attribute benchmarks, we demonstrate consistent and significant performance improvements over the state-of-the-art techniques. In addition, we compile the largest multi-attribute image retrieval dateset to date, including 126 fully labeled query attributes and 6,000 weak attributes of 0.26 million images.
Starting Page	2949
Ending Page	2956
File Size	348206
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphical models Mathematical model Yttrium Image retrieval Training Equations Humans
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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