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Partition selection with sparse autoencoders for content based image classification

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Abstract

Managing colossal image datasets with large dimensional hand-crafted features is no more feasible in most of the cases. Content based image classification (CBIC) of these large image datasets calls for the need of dimensionality reduction of features extracted for the purpose. This paper identifies the escalating challenges in the discussed domain and introduces a technique of feature dimension reduction by means of identifying region of interest in a given image with the use of reconstruction errors computed by sparse autoencoders. The automated process identifies the significant regions in an image for feature extraction. It not only improves the dimension of useful features but also contributes to increased classification results compared to earlier approaches. The reduction in number of one kind of features easily makes space for the inclusion of other features whose fusion facilitates improved classification performance compared to individual feature extraction techniques. Two different datasets, i.e. Wang dataset and Corel 5K dataset have been used for the experiments. State-of-the-art classifiers, i.e. Support Vector Machine and Extreme Learning Machine are used for CBIC. The proposed techniques are evaluated and compared in the context of both the classifiers and analysis of results suggests the appropriateness of the proposed methods for real time applications.

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Acknowledgements

We appreciate the suggestions and comments of the anonymous reviewers which have helped us raise the standard of the paper significantly.

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Authors and Affiliations

  1. Department of Information Technology, Xavier Institute of Social Service, Ranchi, India

    Rik Das

  2. Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

    Ekta Walia

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  1. Rik Das
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  2. Ekta Walia
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Correspondence to Rik Das.

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Das, R., Walia, E. Partition selection with sparse autoencoders for content based image classification. Neural Comput & Applic 31, 675–690 (2019). https://doi.org/10.1007/s00521-017-3099-0

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