Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.54
Hierarchical Anomality Detection based on Situation
Nishio, Shuichi, Advanced Telecommunication Res. Inst. International
Okamoto, Hiromi, Nara Women’s Univ.
Babaguchi, Noboru, Osaka Univ.
In this paper, we propose a novel anomality detection method based on external situational information and hierarchical
analysis of behaviors. Past studies model normal behaviors to detect anomality as outliers. However, normal behaviors tend
to differ by situations. Our method combines a set of simple classifiers with pedestrian trajectories as inputs. As mere path
information is not sufficient for detecting anomality, trajectories are first decomposed into hierarchical features of different
abstract levels and then applied to appropriate classifiers corresponding to the situation it belongs to. Effects of the methods
are tested using real environment data.
09:00-11:10, Paper TuAT8.55
Image Classification using Subgraph Histogram Representation
Ozdemir, Bahadir, Bilkent Univ.
Aksoy, Selim, Bilkent Univ.
We describe an image representation that combines the representational power of graphs with the efficiency of the bag-ofwords
model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial
arrangements. Then, each graph is represented with a histogram of sub graphs selected using a frequent subgraph mining algorithm
in the whole data. Using the sub graphs as the visual words of the bag-of-words model and transforming of the
graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments
using images cut from a large satellite scene show the effectiveness of the proposed representation in classification
of complex types of scenes into eight high-level semantic classes.
09:00-11:10, Paper TuAT8.56
Oriented Boundary Graph: A Framework to Design and Implement 3D Segmentation Algorithms
Baldacci, Fabien, Univ. de Bordeaux
Braquelaire, Achille, Univ. de Bordeaux
Domenger, Jean Philippe, Univ. de Bordeaux
In this paper we show the interest of a topological model to represent 3D segmented image which is a good compromise between
the complete but time consuming representations and the partial but not expressive enough ones. We show that this
model, called Oriented Boundary Graph, provides an effective framework for both volumic image analysis and segmentation.
The Oriented Boundary Graph provides an efficient implementation of a set of primitives suitable for the design complex
segmentation algorithms and to implement the computation of the segmented image characteristics needed by such algorithms.
We first present the framework and give the time complexity of its main primitives. Then, we give some examples of the use
of this framework in order to efficiently design non-trivial image analysis operations and image segmentation algorithms.
Those examples are applied on 3D CT-scan data.
09:00-11:10, Paper TuAT8.57
Hierarchical Segmentation of Complex Structures
Akcay, Huseyin Gokhan, Bilkent Univ.
Aksoy, Selim, Bilkent Univ.
Soille, Pierre, Ec. Joint Res. Centre
We present an unsupervised hierarchical segmentation algorithm for detection of complex heterogeneous image structures
that are comprised of simpler homogeneous primitive objects. An initial segmentation step produces regions corresponding
to primitive objects with uniform spectral content. Next, the transitions between neighboring regions are modeled and clustered.
We assume that the clusters that are dense and large enough in this transition space can be considered as significant.
Then, the neighboring regions belonging to the significant clusters are merged to obtain the next level in the hierarchy. The
experiments show that the algorithm that iteratively clusters and merges region groups is able to segment high-level complex
structures in a hierarchical manner.
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