Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.12
Differential Morphological Decomposition Segmentation: A Multi-Scale Object based Image Description
Gueguen, Lionel, JRC – European Commission
Soille, Pierre, Ec. Joint Res. Centre
Pesaresi, Martino, Ec. Joint Res. Centre
In order to describe, to extract image information content, segmentation is a well-known approach to represent the information
in terms of objects. Image segmentation is a common image processing technique aiming at disintegrating an
image into a partition of its support. Hierarchical of fuzzy segmentation are extension of segmentation definition, in order
to provide a covering of the image support with overlapping segments. In this paper, we propose a novel approach for
breaking up an image into multi-scale overlapping objects. The image is decomposed by granulometry or differential morphological
pyramid, resulting in a discrete scale-space representation. Then, the scale-space transform is segmented by a
region based method. Projecting the obtained scale-space partition into space constitutes the disintegrated image representation,
which enables a multi-scale object based image description.
09:00-11:10, Paper TuAT8.13
Efficient Learning to Label Images
Jia, Ke, Australian National Univ. National ICT Australia
Cheng, Li, NICTA
Liu, Nianjun, NICTA
Wang, Lei, The Australian National Univ.
Conditional random field methods (CRFs) have gained popularity for image labeling tasks in recent years. In this paper,
we describe an alternative discriminative approach, by extending the large margin principle to incorporate spatial correlations
among neighboring pixels. In particular, by explicitly enforcing the sub modular condition, graph-cuts is conveniently
integrated as the inference engine to attain the optimal label assignment efficiently. Our approach allows learning
a model with thousands of parameters, and is shown to be capable of readily incorporating higher-order scene context.
Empirical studies on a variety of image datasets suggest that our approach performs competitively compared to the stateof-the-art
scene labeling methods.
09:00-11:10, Paper TuAT8.14
NAVIDOMASS: Structural-Based Approaches towards Handling Historical Documents
Jouili, Salim, LORIA
Coustaty, Mickaël, Univ. of La Rochelle
Tabbone, Salvatore, Univ. Nancy 2-LORIA
Ogier, Jean-Marc, Univ. de la Rochelle
In the context of the NAVIDOMASS project, the problematic of this paper concerns the clustering of historical document
images. We propose a structural-based framework to handle the ancient ornamental letters data-sets. The contribution,
firstly, consists of examining the structural (i.e. graph) representation of the ornamental letters, secondly, the graph matching
problem is applied to the resulted graph-based representations. In addition, a comparison between the structural (graphs)
and statistical (generic Fourier descriptor) techniques is drawn.
09:00-11:10, Paper TuAT8.15
Median Graph Shift: A New Clustering Algorithm for Graph Domain
Jouili, Salim, LORIA
Tabbone, Salvatore, Univ. Nancy 2-LORIA
Lacroix, Vinciane, Royal Military Acad. Belgium
In the context of unsupervised clustering, a new algorithm for the domain of graphs is introduced. In this paper, the key idea
is to adapt the mean-shift clustering and its variants proposed for the domain of feature vectors to graph clustering. These algorithms
have been applied successfully in image analysis and computer vision domains. The proposed algorithm works in
an iterative manner by shifting each graph towards the median graph in a neighborhood. Both the set median graph and the
generalized median graph are tested for the shifting procedure. In the experiment part, a set of cluster validation indices are
used to evaluate our clustering algorithm and a comparison with the well-known Kmeans algorithm is provided.
09:00-11:10, Paper TuAT8.16
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