Abstract book (pdf) - ICPR 2010

In this paper we propose a new multi-view semi-supervised learning algorithm called Local Co-Training(LCT). The proposed
algorithm employs a set of local models with vector outputs to model the relations among examples in a local region
on each view, and iteratively refines the dominant local models (i.e. the local models related to the unlabeled examples
chosen for enriching the training set) using unlabeled examples by the co-training process. Compared with previous cotraining
style algorithms, local co-training has two advantages: firstly, it has higher classification precision by introducing
local learning; secondly, only the dominant local models need to be updated, which significantly decreases the computational
load. Experiments on WebKB and Cora datasets demonstrate that LCT algorithm can effectively exploit unlabeled
data to improve the performance of web page classification.
13:30-16:30, Paper WeBCT8.8
Robust Face Recognition using Multiple Self-Organized Gabor Features and Local Similarity Matching
Aly, Saleh, Kyushu Univ.
Shimada, Atsushi, Kyushu Univ.
Tsuruta, Naoyuki, Fukuoka Univ.
Taniguchi, Rin-Ichiro, Kyushu Univ.
Gabor-based face representation has achieved enormous success in face recognition. However, one drawback of Gaborbased
face representation is the huge amount of data that must be stored. Due to the nonlinear structure of the data obtained
from Gabor response, classical linear projection methods like principal component analysis fail to learn the distribution
of the data. A nonlinear projection method based on a set of self-organizing maps is employed to capture this nonlinearity
and to represent face in a new reduced feature space. The Multiple Self-Organized Gabor Features (MSOGF) algorithm
is used to represent the input image using all winner indices from each SOM map. A new local matching algorithm based
on the similarity between local features is also proposed to classify unlabeled data. Experimental results on FERET database
prove that the proposed method is robust to expression variations.
13:30-16:30, Paper WeBCT8.9
Exploring Pattern Selection Strategies for Fast Neural Network Training
Vajda, Szilard, Tech. Univ. of Dortmund
Fink, Gernot, TU Dortmund Univ.
Nowadays, the usage of neural network strategies in pattern recognition is a widely considered solution. In this paper we
propose three different strategies to select more efficiently the patterns for a fast learning in such a neural framework by
reducing the number of available training patterns. All the strategies rely on the idea of dealing just with samples close to
the decision boundaries of the classifiers. The effectiveness (accuracy, speed) of these methods is confirmed through different
experiments on the MNIST handwritten digit data [1], Bangla handwritten numerals [2] and the Shuttle data from
the UCI machine learning repository [3].
13:30-16:30, Paper WeBCT8.10
The Detection of Concept Frames using Clustering Multi-Instance Learning
Tax, David, Delft Univ. of Tech.
Hendriks, E. , Delft Univ. of Tech.
Valstar, Michel, Imperial Coll.
Pantic, M., Imperial Coll.
The classification of sequences requires the combination of information from different time points. In this paper the detection
of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos
show that it is not always required to model the sequences fully, but that the presence of specific frames (the concept
frame) can be sufficient for a reliable detection of certain facial expression classes. For the detection of these concept
frames a standard classifier is often sufficient, although a more advanced clustering approach performs better in some
cases.
13:30-16:30, Paper WeBCT8.11
Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation
Gripton, Adam, Heriot-Watt Univ.
Lu, Weiping, Heriot-Watt Univ.
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