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Automated Flower Region Segmentation from Color Images

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

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

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Abstract

Segmentation of flower region is one of the important parts in flower image retrieval. Flower segmentation is the challenging task because of variety in colors and different light conditions. In this paper fully automated flower segmentation method using HSV and texture features is proposed. The texture features namely contrast, correlation, Energy and Homogeneity computed from Hue and Value image. Further these features are used to decide the value for global threshold for converting the image into binary image. The proposed method is tested on flower image dataset contain 102 flower classes and also tested on Oxford-102. The experimental results shows that proposed method is effectively segment the flower regions from images.

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Authors and Affiliations

  1. Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur, India

    Monali Y. Khachane

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  1. Monali Y. Khachane
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Correspondence to Monali Y. Khachane .

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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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Khachane, M.Y. (2019). Automated Flower Region Segmentation from Color Images. 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_33

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  • DOI: https://doi.org/10.1007/978-981-13-9181-1_33

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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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