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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 49–59Cite as

Material Classification Using Color and Texture Features

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1035))

Abstract

Classification of texture is a very tedious problem in computer vision and pattern recognition. In this problem, the material is assigned to particular class of texture using its properties. This paper used both color and texture features to improve the recognition performance of Flickr Material Database (FMD). Authors described method of combining Color features (RGB), Luminance and Texture features. Gray-Level Co-occurrence Matrix (GLCM) is used to extract Texture features. The classification using, K-Nearest Neighbors (KNN) classifier is discussed with the experimental results.

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Authors and Affiliations

  1. School of Computer Sciences, Kaviyatri Bahinabai Chaudhari North Maharashtra University, Jalgaon, India

    Shubhangi S. Sapkale & Manoj P. Patil

Authors
  1. Shubhangi S. Sapkale
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  2. Manoj P. Patil
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Corresponding author

Correspondence to Shubhangi S. Sapkale .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Sapkale, S.S., Patil, M.P. (2019). Material Classification Using Color and Texture Features. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_5

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  • DOI: https://doi.org/10.1007/978-981-13-9181-1_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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