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A Novel Algorithm for Salient Region Detection

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Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1241))

Abstract

Salient region is the most prominent object in the scene which attracts to the human vision system. This paper presents a novel algorithm that is based on the separated Red, Green and Blue colour channels. Most prominent regions of all the three channels of RGB colour model are extracted using mean value of the respective channels. Pixels of extracted salient region of RGB channels are counted and then some specified rules are applied over these channels to generate final saliency map. To evaluate the performance of the proposed novel algorithm, a standard dataset MSRA-B has been used. The proposed algorithm presents better result and outperformed to the existing approaches.

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Authors and Affiliations

  1. Computer Engineering and Applications, IET, GLA University, Mathura, Uttar Pradesh, India

    Rajesh Kumar Tripathi

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  1. Rajesh Kumar Tripathi
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Correspondence to Rajesh Kumar Tripathi .

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Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Tripathi, R.K. (2020). A Novel Algorithm for Salient Region Detection. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_33

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6317-1

  • Online ISBN: 978-981-15-6318-8

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