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Decision Fusion for Classification of Content Based Image Data

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Transactions on Computational Science XXIX

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 10220))

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Abstract

Information recognition by means of content based image identification has emerged as a prospective alternative to recognize semantically analogous images from huge image repositories. Critical success factor for content based recognition process has been reliant on efficient feature vector extraction from images. The paper has introduced two novel techniques of feature extraction based on image binarization and Vector Quantization respectively. The techniques were implemented to extract feature vectors from three public datasets namely Wang dataset, Oliva and Torralba (OT-Scene) dataset and Corel dataset comprising of 14,488 images on the whole. The classification decisions with multi domain features were standardized with Z score normalization for fusion based identification approach. Average increase of 30.71% and 28.78% in precision were observed for classification and retrieval respectively when the proposed methodology was compared to state-of-the art techniques.

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Authors and Affiliations

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

    Rik Das

  2. Department of Information Technology, Pimpri Chinchwad College of Engineering, Pune, India

    Sudeep Thepade

  3. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Saurav Ghosh

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  1. Rik Das
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  2. Sudeep Thepade
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  3. Saurav Ghosh
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Correspondence to Rik Das .

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Editors and Affiliations

  1. University of Calgary , Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems , Tallinn, Estonia

    C.J. Kenneth Tan

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Das, R., Thepade, S., Ghosh, S. (2017). Decision Fusion for Classification of Content Based Image Data. In: Gavrilova, M., Tan, C. (eds) Transactions on Computational Science XXIX. Lecture Notes in Computer Science(), vol 10220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54563-8_7

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  • DOI: https://doi.org/10.1007/978-3-662-54563-8_7

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