Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.41
A Variational Bayesian EM Algorithm for Tree Similarity
Takasu, Atsuhiro, National Inst. of Informatics
Fukagawa, Daiji, National Inst. of Informatics
Akutsu, Tatsuya, Kyoto Univ.
In recent times, a vast amount of tree-structured data has been generated. For mining, retrieving, and integrating such data,
we need a fine-grained tree similarity measure that can be adapted to objective data. To achieve this goal, this paper (1)
proposes a probabilistic generative model that generates pairs of similar trees, and (2) derives a learning algorithm for estimating
the parameters of the model based on the variational Bayesian expectation maximization (VBEM) method. This
method can handle rooted, ordered, and labeled trees. We show that the tree similarity model obtained via the BEM technique
performs better than that obtained via maximum likelihood estimation by tuning the hyper parameters.
09:00-11:10, Paper TuAT8.42
Enhancing Image Classification with Class-Wise Clustered Vocabularies
Wojcikiewicz, Wojciech, Fraunhofer Inst. FIRST
Kawanabe, Motoaki, Fraunhofer FIRST and TU Berlin
Binder, Alexander, Fraunhofer Inst. FIRST, Berlin
In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification.
Their performance highly relies on creating a good visual vocabulary from a set of image features (e.g. SIFT). For realworld
photo archives such as Flicker, codebooks with larger than a few thousand words are desirable, which is infeasible
by the standard k-means clustering. In this paper, we propose a two-step procedure which can generate more informative
codebooks efficiently by class-wise k-means and a novel procedure for word selection. Our approach was compared favorably
to the standard k-means procedure on the PASCAL VOC data sets.
09:00-11:10, Paper TuAT8.43
Efficiently Computing Optimal Consensus of Digital Line Fitting
Kenmochi, Yukiko, Univ. Paris-Est
Buzer, Lilian, ESIEE
Talbot, Hugues, ESIEE
Given a set of discrete points in a 2D digital image containing noise, we formulate our problem as robust digital line
fitting. More precisely, we seek the maximum subset whose points are included in a digital line, called the optimal consensus.
The paper presents an efficient method for exactly computing the optimal consensus by using the topological
sweep, which provides us with the quadratic time complexity and the linear space complexity with respect to the number
of input points.
09:00-11:10, Paper TuAT8.44
Learning a Joint Manifold Representation from Multiple Data Sets
Torki, Marwan, Rutgers Univ.
Elgammal, Ahmed, Rutgers Univ.
Lee, Chan-Su, Yeungnam Univ.
The problem we address in the paper is how to learn a joint representation from data lying on multiple manifolds. We are
given multiple data sets and there is an underlying common manifold among the different data set. We propose a framework
to learn an embedding of all the points on all the manifolds in a way that preserves the local structure on each manifold
and, in the same time, collapses all the different manifolds into one manifold in the embedding space, while preserving
the implicit correspondences between the points across different data sets. The proposed solution works as extensions to
current state of the art spectral-embedding approaches to handle multiple manifolds.
09:00-11:10, Paper TuAT8.45
A Multi-Scale Approach to Decompose a Digital Curve into Meaningful Parts
Nguyen, Thanh Phuong, LORIA
Debled-Rennesson, Isabelle, LORIA – Nancy Univ.
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