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Significance of processing chrominance information for scene classification: a review

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Abstract

The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems.

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Notes

  1. The terms, image classification and scene classification are interchangeably used in the context of the works presented in this paper.

  2. This method is represented as ‘rgb2gray’ throughout the report.

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  1. Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    V. Sowmya, D. Govind & K. P. Soman

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  1. V. Sowmya
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  2. D. Govind
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  3. K. P. Soman
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Correspondence to V. Sowmya.

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Sowmya, V., Govind, D. & Soman, K.P. Significance of processing chrominance information for scene classification: a review. Artif Intell Rev 53, 811–842 (2020). https://doi.org/10.1007/s10462-018-09678-0

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