  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.
