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Normalised Euclidean Distance Based Image Retrieval Using Coefficient Analysis

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 177))

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Abstract

The article presented a novel method based on normalized Euclidean distance using application of discrete wavelet transform and bins intensity measurement, which is then coupled to a parameterized framework for content-based image retrieval. The discrete wavelet transform captures both frequency and location information and make image retrieval efficient. It further facilitates to incorporate recent research work on feature based coefficient distributions. We demonstrate the applicability of the proposed method in the context of color texture retrieval on different image databases and compare retrieval performance to a collection of state-of-the-art approaches in the area. Our experiment results on a large database further include a thorough analysis of computations of the main building blocks and runtime measurements of images.

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Authors and Affiliations

  1. Department of Information Technology, University of RGPV Bhopal Chameli Devi School of Engineering, Indore, India

    Nilofar Khan

  2. Department of Computer Science, University of RGPV Bhopal Govt. Women’s Polytechnic College, Indore, India

    Wasim Khan

Authors
  1. Nilofar Khan
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  2. Wasim Khan
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Corresponding author

Correspondence to Nilofar Khan .

Editor information

Editors and Affiliations

  1. , Department of Computer Science, Jackson State University, John R. Lynch Street 1400, Jackson, 39217, USA

    Natarajan Meghanathan

  2. Wireilla Net Solutions PTY Ltd, Melbourne, Australia

    Dhinaharan Nagamalai

  3. Department of Computer Science & Eng., University of Calcutta, Calcutta, 700 073, India

    Nabendu Chaki

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Khan, N., Khan, W. (2013). Normalised Euclidean Distance Based Image Retrieval Using Coefficient Analysis. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_79

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  • DOI: https://doi.org/10.1007/978-3-642-31552-7_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31551-0

  • Online ISBN: 978-3-642-31552-7

  • eBook Packages: EngineeringEngineering (R0)

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