Abstract book (pdf) - ICPR 2010

We propose a method that analyzes the structure of a large volume of general broadcast video data by the appearance patterns
of near-duplicate video segments. We define six classification rules based on the appearance patterns of near-duplicate video
segments according to their roles, and evaluated them over more than 1,000 hours of actual broadcast video data.
13:30-16:30, Paper WeBCT9.3
Motion Vector based Features for Content based Video Copy Detection
Tasdemir, Kasim, Bilkent Univ.
Cetin, E., Bilkent Univ.
In this article, we propose a motion vector based feature set for Content Based Copy Detection (CBCD) of video clips. Motion
vectors of image frames are one of the signatures of a given video. However, they are not descriptive enough when consecutive
image frames are used because most vectors are too small. To overcome this problem we calculate motion vectors in a lower
frame rate than the actual frame rate of the video. As a result we obtain longer vectors which form a robust parameter set representing
a given video. Experimental results are presented.
13:30-16:30, Paper WeBCT9.4
A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis
Papadopoulos, Georgios Th., Centre for Res. and Tech. Hellas
Mezaris, Vasileios, Centre for Res. and Tech. Hellas
Kompatsiaris, Yiannis, Centre for Res. and Tech. Hellas
Strintzis, Michael-Gerasimos,
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed
method constitutes an extension of the key parts of the authors’ previous work on spatial context utilization, where a Genetic
Algorithm (GA) was introduced for exploiting fuzzy directional relations after performing an initial classification of image regions
to semantic concepts using solely visual information. In the extensions reported in this work, a more elaborate approach
is followed during the spatial knowledge acquisition and modeling process. Additionally, the impact of every resulting spatial
constraint on the final outcome is adaptively adjusted. Experimental results as well as comparative evaluation on three datasets
of varying complexity in terms of the total number of supported semantic concepts demonstrate the efficiency of the proposed
method.
13:30-16:30, Paper WeBCT9.5
Wavelet-Based Texture Retrieval Modeling the Magnitudes of Wavelet Detail Coefficients with a Generalized Gamma Distribution
De Ves Cuenca, Esther, Univ. of Valencia
Benavent, Xaro, Univ. of Valencia
Ruedin, Ana María Clara, Univ. de Buenos Aires
Acevedo, Daniel Germán, Univ. de Buenos Aires
Seijas, Leticia María, Univ. de Buenos Aires
This paper presents a texture descriptor based on the fine detail coefficients at three resolution levels of a traslation invariant
undecimated wavelet transform. First, we consider vertical and horizontal wavelet detail coefficients at the same position as the
components of a bivariate random vector, and the magnitude and angle of these vectors are computed. The magnitudes are modeled
by a Generalized Gamma distribution. Their parameters, together with the circular histograms of angles, are used to characterize
each texture image of the database. The Kullback-Leibler divergence is used as the similarity measurement. Retrieval
experiments, in which we compare two wavelet transforms, are carried out on the Brodatz texture collection. Results reveal the
good performance of this wavelet-based texture descriptor obtained via the Generalized Gamma distribution.
13:30-16:30, Paper WeBCT9.6
3D-Shape Retrieval using Curves and HMM
Tabia, Hedi, Lagis Univ. Lille 1
Daoudi, Mohamed, TELECOM Lille1
Vandeborre, Jean-Philippe, Univ. of Lille 1
Colot, Olivier, Univ. Lille 1
In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step.
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