Abstract book (pdf) - ICPR 2010

This paper addresses the problem of improving the accuracy of character recognition with a limited quantity of data. The
key ideas are twofold. One is distortion-tolerant template matching via hierarchical global/partial affine transformation
(GAT/PAT) correlation to absorb both linear and nonlinear distortions in a parametric manner. The other is use of multiple
templates per category obtained by k-means clustering in a gradient feature space for dealing with topological distortion.
Recognition experiments using the handwritten numerical database IPTP CDROM1B show that the proposed method
achieves a much higher recognition rate of 97.9% than that of 85.8% obtained by the conventional, simple correlation
matching with a single template per category. Furthermore, comparative experiments show that the k-NN classification
using the tangent distance and the GAT correlation technique achieves recognition rates of 97.5% and 98.7%, respectively.
17:00-17:20, Paper WeCT7.5
Structure Adaptation of HMM Applied to OCR
Ait Mohand, Kamel, Univ. of Rouen
Paquet, Thierry, Univ. of Rouen
Ragot, Nicolas, Univ. François Rabelais Tours
Heutte, Laurent, Univ. of Rouen
In this paper we present a new algorithm for the adaptation of Hidden Markov Models (HMM models). The principle of
our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation
stage of the parameters. This algorithm is applied to the recognition of printed characters to adapt the character models of
a poly font general purpose character recognizer to new fonts of characters, never seen during training. A comparison of
the results with those of MAP classical adaptation scheme show a slight increase in the recognition performance.
WeBCT8 Upper Foyer
SVM, NN, Kernel and Learning; Object Detection and Recognition Poster Session
Session chair: Ross, Arun (West Virginia Univ.)
13:30-16:30, Paper WeBCT8.1
Multi-Class Pattern Classification in Imbalanced Data
Ghanem, Amal Saleh, Univ. of Bahrain
Venkatesh, Svetha, Curtin Univ. of Tech.
West, Geoff, Curtin Univ. of Tech.
The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However,
in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few
training examples compared for other classes. In this paper we present our research in learning from imbalanced multiclass
data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals
from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the twoclass
problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational
and non-relational domains.
13:30-16:30, Paper WeBCT8.2
Deep Quantum Networks for Classification
Zhou, Shusen, Harbin Inst. of Tech.
Chen, Qingcai, Harbin Inst. of Tech.
Wang, Xiaolong, Harbin Inst. of Tech.
This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN
inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of
DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new
Euclidean space. The parameter of DQN is initialized through greedy layer-wise unsupervised learning. Then, the parameter
space of the deep architecture and quantum representation are refined by supervised learning based on the global gradient-descent
procedure. An exponential loss function is introduced in this paper to guide the supervised learning procedure.
Experiments conducted on standard datasets show that DQN outperforms other feed forward neural networks and neurofuzzy
classifiers.
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