Combining Model-Based and Discriminative Approaches in a Modular Two-Stage Classification System: Application to Isolated Handwritten Digit Recognition

Milgram, Jonathan; Sabourin, Robert; Cheriet, Mohamed

doi:10.1142/9789812834461_0011

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Multiple classifier combination is a technique that combines the decisions of different classifiers that are trained to solve the same problem, but make independent errors. Many studies of combination systems have been attempted in different applications of Pattern-Recognition (PR) as handwritten character recognition [17], speaker recognition [18], face identification [26], etc. Recently, the concept of Multiple Classifier Systems (MCSs) was proposed as an alternative to obtain high classification performance, i.e., in the sense of a high classification rate based on the decisions of a set of classifiers.…”

Section: Introductionmentioning

confidence: 99%

Multiple Classifiers Approach based on DynamicSelection to Maximize Classification Performance

Ayad¹,

Syed-Mouchaweh²

2011

IJMLC

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In the past decade, many researchers have employed various methodologies to combine decisions of multiple classifiers in order to achieve high pattern recognition performance. However, two main strategies of combination are possible. The first strategy uses the different opinions of classifiers to make the final decision; it corresponds to classifiers fusion. The second strategy uses the decisions of one or more better classifiers in a specific region of feature space; it corresponds to the selection of classifiers. In this paper, we propose a dynamic multiple classifiers selection system organized in two levels of decision. Two classification methods are used: Semi-Supervised Fuzzy Pattern Matching (SSFPM) and Support Vector Machines (SVM). SSFPM is used to determine the ambiguous regions. Then, the patterns located in these regions are classified by SVM. The detection of the occurrence of new classes and the learning of their membership functions are achieved online using SSFPM. This combination helps to overcome the drawbacks of the both methods by gathering their advantages leading to increase the classification performance.

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Section: Introductionmentioning

confidence: 99%

Multiple Classifiers Approach based on DynamicSelection to Maximize Classification Performance

Ayad¹,

Syed-Mouchaweh²

2011

IJMLC

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Deformation Models for Image Recognition

Keysers

Deselaers

Gollan

et al. 2007

IEEE Trans. Pattern Anal. Mach. Intell.

215

166

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We present the application of different nonlinear image deformation models to the task of image recognition. The deformation models are especially suited for local changes as they often occur in the presence of image object variability. We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks. We show experimentally that the model performs very well for four different handwritten digit recognition tasks and for the classification of medical images, thus showing high generalization capacity. In particular, an error rate of 0.54 percent on the MNIST benchmark is achieved, as well as the lowest reported error rate, specifically 12.6 percent, in the 2005 international ImageCLEF evaluation of medical image categorization.

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Combining Model-Based and Discriminative Approaches in a Modular Two-Stage Classification System: Application to Isolated Handwritten Digit Recognition

Abstract: Abstract

Cited by 2 publications

References 5 publications

Multiple Classifiers Approach based on DynamicSelection to Maximize Classification Performance

Multiple Classifiers Approach based on DynamicSelection to Maximize Classification Performance

Deformation Models for Image Recognition

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