Abstract book (pdf) - ICPR 2010

This work contributes to part-based object detection and recognition by introducing an enhanced method for local part
detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple hypothesis
testing. In the present work, our main contribution is the introduction of a canonical object space, where objects
are represented in their ``expected pose and visual appearance’’. The canonical space circumvents the problem of geometric
image normalisation prior to feature extraction. In addition, we define a compact set of Gabor filter parameters, from
where the optimal values can be easily devised. These enhancements make our method an attractive landmark detector
for part-based object detection and recognition methods.
14:30-14:50, Paper TuBT3.4
Multiple-Shot Person Re-Identification by HPE Signature
Bazzani, Loris, Univ. of Verona
Cristani, Marco, Univ. of Verona
Perina, Alessandro, Univ. of Verona
Farenzena, Michela, Univ. of Verona
Murino, Vittorio, Univ. of Verona
In this paper, we propose a novel appearance-based method for person re-identification, that condenses a set of frames of
the same individual into a highly informative signature, called Histogram Plus Epitome, HPE. It incorporates complementary
global and local statistical descriptions of the human appearance, focusing on the overall chromatic content, via
histograms representation, and on the presence of recurrent local patches, via epitome estimation. The matching of HPEs
provides optimal performances against low resolution, occlusions, pose and illumination variations, defining novel stateof-the-art
results on all the datasets considered.
14:50-15:10, Paper TuBT3.5
Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles
Benedek, Csaba, Computer and Automation Res. Inst. Hungarian
Descombes, Xavier, INRIA
Zerubia, Josiane, INRIA
In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data
heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from
different elementary feature based modules. A global optimization process attempts to find the optimal configuration of
buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building
parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the
results are matched against two state-of-the-art techniques.
TuBT4 Dolmabahçe Hall A
Model Selection and Clustering Regular Session
Session chair: Shapiro, Linda (Univ. of Washington)
13:30-13:50, Paper TuBT4.1
A Relationship between Generalization Error and Training Samples in Kernel Regressors
Tanaka, Akira, Hokkaido Univ.
Imai, Hideyuki, Hokkaido Univ.
Kudo, Mineichi, Hokkaido Univ.
Miyakoshi, Masaaki, Hokkaido Univ.
A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization
error can be decomposed into two components. One is a distance between an unknown true function and an
adopted model space. The other is a distance between an estimated function and the orthogonal projection of the unknown
true function onto the model space. In our previous work, we gave a framework to evaluate the first component. In this
paper, we theoretically analyze the second one and show that a larger set of training samples usually causes a larger generalization
error.
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