Abstract book (pdf) - ICPR 2010

optimization problem which returns a global optimal solution. Experimental results on several databases show that the
learned distance metric improves the performances of the subsequent classification and clustering algorithms.
09:20-09:40, Paper ThAT4.2
A Comparitive Study on the Use of an Ensemble of Feature Extractors for the Automatic Design of Local Image Descriptors
Carneiro, Gustavo, Tech. Univ. of Lisbon
The use of an ensemble of feature spaces trained with distance metric learning methods has been empirically shown to be
useful for the task of automatically designing local image descriptors. In this paper, we present a quantitative analysis
which shows that in general, nonlinear distance metric learning methods provide better results than linear methods for automatically
designing local image descriptors. In addition, we show that the learned feature spaces present better results
than state of- the-art hand designed features in benchmark quantitative comparisons. We discuss the results and suggest
relevant problems for further investigation.
09:40-10:00, Paper ThAT4.3
A Study on Combining Sets of Differently Measured Dissimilarities
Ibba, Alessandro, Delft Univ. of Tech.
Duin, Robert, Delft Univ. of Tech.
Lee, Wan-Jui, Delft Univ. of Tech.
The ways distances are computed or measured enable us to have different representations of the same objects. In this paper
we want to discuss possible ways of merging different sources of information given by differently measured dissimilarity
representations. We compare here a simple averaging scheme [1] with dissimilarity forward selection and other techniques
based on the learning of weights of linear and quadratic forms. Our general conclusion is that, although the more advanced
forms of combination cannot always lead to better classification accuracies, combining given distance matrices prior to
training is always worthwhile. We can thereby suggest which combination schemes are preferable with respect to the problem
data.
10:00-10:20, Paper ThAT4.4
Efficient Kernel Learning from Constraints and Unlabeled Data
Soleymani Baghshah, Mahdieh, Sharif Univ. of Tech.
Bagheri Shouraki, Saeed, Sharif Univ. of Tech.
Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semisupervised
learning tasks. In the last few years, several methods have been proposed for metric learning when must-link
and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis
metrics, some recently introduced methods have tried to learn more flexible distance metrics using a kernelbased
approach. In this paper, we consider the problem of kernel learning from both pairwise constraints and unlabeled
data. We propose a method that adapts a flexible distance metric via learning a nonparametric kernel matrix. We formulate
our method as an optimization problem that can be solved efficiently. Experimental evaluations show the effectiveness of
our method compared to some recently introduced methods on a variety of data sets.
10:20-10:40, Paper ThAT4.5
Semi-Supervised Graph Learning: Near Strangers or Distant Relatives
Chen, Weifu, Sun Yat-sen Univ.
Feng, Guocan, Sun Yat-Sen Univ.
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and
clustering, using the most of prior knowledge from limited pairwise constraints. We extend instance-level constraints to
space-level constraints to construct a more meaningful graph. By decomposing the (normalized) Laplacian matrix of this
graph, to use the bottom eigenvectors leads to new representations of the data, which are hoped to capture the intrinsic
structure. The proposed method improves the previous constrained learning methods. Furthermore, to achieve a given
clustering accuracy, fewer constraints are required in our method. Experimental results demonstrate the advantages of the
proposed method.
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