Weak attributes for large-scale image retrieval
Summary
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
multi-attribute 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.
Performance
evaluation on a-PASCAL and a-Yahoo
The
a-TRECVID dataset is available here
Matlab code
for the proposed semi-supervised graphical model
Please direct any questions to Felix X. Yu (yuxinnan (at)
ee.columbia.edu)
Publications
Felix X. Yu; Rongrong Ji; Ming-Hen Tsai; Guangnan
Ye; Shih-Fu Chang.
Weak attributes for large-scale image
retrieval, CVPR 2012 [PDF]
[Supplementary
Material] [Poster]
Felix X. Yu; Rongrong Ji; Ming-Hen Tsai; Guangnan
Ye; Shih-Fu Chang.
Experiments of image retrieval using weak attributes,
Technical Report # CUCS 005-12 [PDF]