A method of combining multiple experts for the recognition of unconstrained handwritten numerals

Huang, Yongliang; Suen, Ching Y.

doi:10.1109/34.368145

Cited by 492 publications

(200 citation statements)

References 8 publications

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“…These were Naive Bayes (NB), the Behavior Knowledge Space method (BKS) (Huang & Suen, 1995); maximum (MAX), minimum, 5 average (AVR) and product (PRO) from the simple methods; and the decision templates (DT) method (Kuncheva et al, 2001). Table 7 shows the rank correlation coefficients between the measures and the diversity measures and the improvement of the accuracy, i.e., P team − P max .…”

Section: Feature Subspace Methodsmentioning

confidence: 99%

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2003

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Abstract. Diversity among the members of a team of classifiers is deemed to be a key issue in classifier combination. However, measuring diversity is not straightforward because there is no generally accepted formal definition. We have found and studied ten statistics which can measure diversity among binary classifier outputs (correct or incorrect vote for the class label): four averaged pairwise measures (the Q statistic, the correlation, the disagreement and the double fault) and six non-pairwise measures (the entropy of the votes, the difficulty index, the Kohavi-Wolpert variance, the interrater agreement, the generalized diversity, and the coincident failure diversity). Four experiments have been designed to examine the relationship between the accuracy of the team and the measures of diversity, and among the measures themselves. Although there are proven connections between diversity and accuracy in some special cases, our results raise some doubts about the usefulness of diversity measures in building classifier ensembles in real-life pattern recognition problems.

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Section: Feature Subspace Methodsmentioning

confidence: 99%

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2003

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“…A Bayesian approach has also been used in Consensus based classification of multisource remote sensing data [10,9,19], outperforming conventional multivariate methods for classification. To overcome the problem of the independence assumption (that is unrealistic in most cases), the Behavior-Knowledge Space (BKS) method [56] considers each possible combination of class labels, filling a look-up table using the available data set, but this technique requires a huge volume of training data.…”

Section: Non-generative Ensemblesmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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Abstract. Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

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“…However for complex tasks such as face recognition, it is often the case that no single feature modality is rich enough to capture all of the classification information available in the image. Finding and combining complementary feature sets has thus become an active research topic in pattern recognition, with successful applications in many challenging tasks including handwritten character recognition [9] and face recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Tan

Triggs

Analysis and Modeling of Faces and Gestures

229

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Abstract. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

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A method of combining multiple experts for the recognition of unconstrained handwritten numerals

Cited by 492 publications

References 8 publications

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Ensembles of Learning Machines

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

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