Exploring the significance of using perceptually relevant image decolorization method for scene classification

TL;DR

This paper demonstrates that incorporating perceptually relevant chrominance information through an improved decolorization method enhances scene classification accuracy, outperforming existing algorithms and validating the importance of chrominance in grayscale image processing.

Contribution

It introduces a novel decolorization algorithm using C2G-SSIM and SVD that better preserves chrominance information, improving scene classification performance.

Findings

Proposed decolorization outperforms 8 benchmark algorithms in quality assessment.

Inclusion of chrominance improves scene classification accuracy.

Combining proposed and conventional methods further enhances results.

Abstract

A color image contains luminance and chrominance components representing the intensity and color information respectively. The objective of the work presented in this paper is to show the significance of incorporating the chrominance information for the task of scene classification. An improved color-to-grayscale image conversion algorithm by effectively incorporating the chrominance information is proposed using color-to-gay structure similarity index (C2G-SSIM) and singular value decomposition (SVD) to improve the perceptual quality of the converted grayscale images. The experimental result analysis based on the image quality assessment for image decolorization called C2G-SSIM and success rate (Cadik and COLOR250 datasets) shows that the proposed image decolorization technique performs better than 8 existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component in scene classification task is demonstrated using the deep belief network (DBN) based image classification system developed using dense scale invariant feature transform (SIFT) as features. The levels of chrominance information incorporated by the proposed image decolorization technique is confirmed by the improvement in the overall scene classification accuracy . Also, the overall scene classification performance is improved by the combination of models obtained using the proposed and the conventional decolorization methods.