The Chinese University of Hong Kong
Steven C.H. Hoi†, Wei Liu†, Michael R. Lyu†, Wei-Ying Ma‡
†The Chinese University of Hong Kong
‡Microsoft Research Asia
• Distance metrics are essential for image retrieval.
• Learning distance metrics from contextual
constraints is critical to bridge the semantic gaps of
image retrieval.
• Traditional distance metric learning usually study
.
linear
distance metrics, which may not be effective
for image retrieval.
• A new distance metric learning method is proposed
for image retrieval.
• We developed two algorithms, Discriminative
Component Analysis (DCA) and Kernel DCA for
learning metrics from pairwise constraints.
• Empirical evaluations have been performed for
image
g retrieval.
Main Ideas
• Improving Relevance Component Analysis (RCA)
by using the dissimilar contextual constraints.
• Looking
L ki for
f the
th mostt di
discriminative
i i ti transformation
t
f
ti
for metric learning.
Covariance matrix between data chunks
Covariance matrix within data chunks
Learning the optimal transformation
20
15
10
5
0
−5
−10
−15
−20
−20
−15
−10
−5
0
5
10
15
20
(a) Original Data Space
20
(a) “Dogs” retrieval
19.5
19
(b) “Butterfly” retrieval
(c) “Roses” retrieval
18.5
18
17.5
17
16.5
20
0
−20
−10
0
20
10
(b) Space via Kernel
0
−1
−2
−3
−4
−5
−6
−7
Experimental results on the 20-Cat dataset
(c) Embedding Space via KDCA
−8
−3
−2
−1
0
1
2
3
The proposed DCA and Kernel DCA are promising for learning
distance metrics from contextual constraints for image retrieval.
CUHK and Microsoft Research Asia
IEEE Computer Vision and Pattern Recognition 2006