Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.33
A Fast Extension for Sparse Representation on Robust Face Recognition
Qiu, Hui-Ning, Sun Yat-sen Univ.
Pham, Duc-Son, Curtin Univ. of Tech.
Venkatesh, Svetha, Curtin Univ. of Tech
Liu, Wanquan, Curtin Univ. of Tech.
Lai, Jian-Huang, Sun Yat-sen Univ.
We extend a recent Sparse Representation-based Classification (SRC) algorithm for face recognition to work on 2D images
directly, aiming to reduce the computational complexity whilst still maintaining performance. Our contributions include:
(1) a new 2D extension of SRC algorithm; (2) an incremental computing procedure which can reduce the eigen decomposition
expense of each 2D-SRC for sequential input data; and (3) extensive numerical studies to validate the proposed
methods.
09:00-11:10, Paper TuAT8.34
A MANOVA of Major Factors of RIU-LBP Feature for Face Recognition
Luo, Jie, Shanghai Univ.
Fang, Yuchun, Shanghai Univ.
Cai, Qiyun, Shanghai Univ.
Local Binary Patterns (LBP) feature is one of the most popular representation schemes for face recognition. The four
factors deciding its effect are the blocking number, image resolution, the sampling radius and sampling density of LBP
operator. Numerous previous researches have taken various groups of value of these factors based on experimental comparisons.
However, which factor among them contributes the most? Numerous revisions are made to the LBP operators
for it is believed that the LBP coding is the most essential factor. Is it true? In this paper, with the very simple and classical
Multivariate Analysis of Variance (MANOVA), we discover that the blocking number contributes the most; though all
four factors have significant effect for recognition rate. In addition, with the same analysis, we disclose the detailed effect
of each factor and their interactions to the precision of LBP features.
09:00-11:10, Paper TuAT8.35
Consistent Estimators of Median and Mean Graph
Jain, Brijnesh J., Berlin Univ. of Tech.
Obermayer, Klaus, Berlin Univ. of Tech.
The median and mean graph are basic building blocks for statistical graph analysis and unsupervised pattern recognition
methods such as central clustering and graph quantization. This contribution provides sufficient conditions for consistent
estimators of true but unknown central points of a distribution on graphs.
09:00-11:10, Paper TuAT8.36
Efficient Encoding of N-D Combinatorial Pyramids
Fourey, Sébastien, GREYC Ensicaen & Univ. of Caen
Brun, Luc, ENSICAEN
Combinatorial maps define a general framework which allows to encode any subdivision of an n-D orientable quasi-manifold
with or without boundaries. Combinatorial pyramids are defined as stacks of successively reduced combinatorial
maps. Such pyramids provide a rich framework which allows to encode fine properties of objects (either shapes or partitions).
Combinatorial pyramids have first been defined in 2D, then extended using n-D generalized combinatorial maps.
We motivate and present here an implicit and efficient way to encode pyramids of n-D combinatorial maps.
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