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Multiple classifier system using classification confidence for texture classification

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Abstract

This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is named as Classification Confidence-based Multiple Classifier Approach (CCMCA). The proposed training based scheme fuses the decisions of two base classifiers (those constitute the classifier ensemble) using their classification confidence to enhance the final classification accuracy. 4-fold cross validation approach is followed to perform experiments on four different texture databases those vary in terms of orientation, number of texture classes and complexity. Apart from its simplicity, the proposed CCMCA method shows better and consistent performance with lowest standard deviation as compared to fixed rule and simple trainable fusion techniques irrespective of the feature set used across all the databases used in the experiment. The performance gain of the proposed CCMCA method over other competing methods is found to be statistically significant.

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Acknowledgments

This work has been supported by Ministry of Communications and Information Technology, Department of Electronics and Information Technology, Govt. of India, Grant number 1(3)2009-ME&TMD and 1(2)2013-ME&TMD/ESDA. Thanks to Indian Institute of Technology Kharagpur for funding our research. Authors are thankful to National Institute of Science and Technology, Berhmapur, Odisha, India 761008 for extending its research facility.

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  1. Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Jatindra Kumar Dash, Sudipta Mukhopadhyay & Rahul Das Gupta

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  1. Jatindra Kumar Dash
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  2. Sudipta Mukhopadhyay
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  3. Rahul Das Gupta
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Correspondence to Jatindra Kumar Dash.

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Dash, J.K., Mukhopadhyay, S. & Gupta, R.D. Multiple classifier system using classification confidence for texture classification. Multimed Tools Appl 76, 2535–2556 (2017). https://doi.org/10.1007/s11042-015-3231-z

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  • DOI: https://doi.org/10.1007/s11042-015-3231-z

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