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© 2013 PosterPresentations.com 2117 Fourth Street , Unit C Berkeley CA 94710 [email protected]
● Image retrieval cleaning aims at improving
the accuracy of web image search and
retrieval. It first removes the irrelevant images
and then re-orders the rest of the images using
the confidence score to be relevant images. A
practicable and efficient cleaning method
should be highly accurate without manual
labeling and pre-processing.
● In this project, a novel algorithm for
unsupervised image retrieval cleaning is
proposed. We delved into the data distribution
and devised three stages noise robust
approaches to solve the problem.
INTRODUCTION
FRAMEWORK
● Graph Building
X={x1,…,xn} represents the features of
retrieved images, the edge matrix W is built as
wij=exp(-||xi-xj||2/2σ2) if i≠j and wii=0. W is
normalized as S=D-1/2WD-1/2, and D is
diagonal with dii=∑j=1:n wij.
● Category-level outlier filtering
X is mapped to a new feature space as
g(xi)=F*(i,∙)T, where F*=(I-αS)-1[y1,…,yi,...yn]
=(I-αS)-1, yi is a column vector with yi=1 and
yothers=0. xi is warped as the i-th row of matrix
F*, and a dominant score is computed as to
sum all dimensions of this row.
Spectral clustering is implemented on g(X) for
k clusters. Clusters with the lowest mean
dominant score are considered as noise.
● Dataset-level noise absorption
There are total t keywords-queries denoted as
Q={q1,…,qi,…,qt} and the retrieval dataset
X(Q)={X(1),…,X(i),…,X(t)}. Linear kernel SVM
classifier is trained for every two categories qi
and qj. The confidence score 𝐶𝑖 𝑥𝑎𝑖
of an
image 𝑥𝑎𝑖
to be the relevant image in its
category is 𝐶𝑖 𝑥𝑎𝑖
= min𝑗≠𝑖
D𝑖, j 𝑥𝑎𝑖
. If the
score is low, 𝑥𝑎𝑖
is regarded as noise to be
filtered and absorbed by qj.
● Category-level confident sample selection
For each category, we want to select the data
points with high density score, i.e. confidence,
and discard the data points with low density
score. In order to calculate the density score,
we use elastic net SVM regression to estimate
the density of data points. The density score of
x is a function of 𝑤𝑇𝑥. We use a modified
version of SMO (Sequential Minimization
Optimization) to train the model.
ALGORITHMS
Figure: Parallel MapReduce based SVM on
Amazon EC2 clusters.
RESULTS
CONCLUSION In this project, we propose a new unsupervised
cleaning algorithm to improve the accuracy of
web image retrieval, by three level irrelevant
images filtering and relevant samples
selection. We warp data into a noise resistant
spectral space to cluster and remove isolated
noise. Then a specially designed cross
category noise absorption algorithm is
proposed to make the dataset cleaner. Finally,
confident samples used for re-ordering are
selected by a regression formulation with
linear kernel and sparsity constraints. Our
approach demonstrates the best performance
among all the recent methods in experimental
comparison on INRIA dataset.
REFERENCES
ACKNOWLEDGEMENTS Great thanks to Dr. Daisy Wang for providing us with an
opportunity to work on this project and present this poster.
Thanks to Amazon for providing the AWS credits. These
credits were utilized for training and testing the SVM
classifiers on Amazon EC2 clusters. We also thank Yottamine
Analitics whose services were used for implementing SVM
classifier on Amazon EC2 clusters.
Many categories of images are retrieved by
different textual-keywords via search engines
and image sharing sites. First, graphs are built
on pairwise images to explore the in-category
data distribution. Isolated nodes are regarded
as irrelevant images. Then, classifiers are
trained on pairwise categories. Images that
classified to other categories are also treated as
irrelevant images. After we remove all
irrelevant images, for each category, images in
dominant clusters, which are most likely to be
relevant images, are used as manifold learning
pseudo queries for re-ordering.
Figure: The flowchart of our three stages
algorithm.
[Spectral Clustering] A. Ng, M. Jordan, and Y. Weiss. On
spectral clustering: Analysis and an algorithm. In NIPS, 2002.
[Elastic Net SVM] G.-B. Ye, Y. Chen, and X. Xie. Efficient
variable selection in support vector machines via the
alternating direction method of multipliers. In ICAIS, 2011.
[SMO] J. Platt, and et al. Sequential minimal optimization: A
fast algorithm for training support vector machines. In
technical report Microsoft Research, 1998.
[INRIA dataset and LogClass] J. Krapac, M. Allan, J. Verbeek,
and F. Juried. Improving web image search results using
query-relative classifiers. In CVPR, 2010.
[Mrank] D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B.
Sch¨olkopf. Ranking on data manifolds. In NIPS, 2004.
[LabelDiag] J. Wang, Y. Jiang, and S. Chang. Label diagnosis
through self tuning for web image search. In CVPR, 2009.
[SpecFilter] W. Liu, Y. Jiang, J. Luo, and S. Chang. Noise
resistant graph ranking for improved web image search. In
CVPR, 2011.
Figure: Examples in INRIA dataset: Top-15
ranked images using (a) the search engine and
(b) our approach.
Figure: INRIA dataset: MAP of ranking order
results over 353 categories.
Department of Computer & Information Science & Engineering, University of Florida
Chen Cao, Yang Peng, Mustaqim Moulavi, Harsh Bhavsar
Unsupervised Cleaning for Web Image Retrieval