Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Detail Preserving Reproduction of Color Images for Monochromats and Dichromats
IEEE Computer Graphics and Applications
Color2Gray: salience-preserving color removal
ACM SIGGRAPH 2005 Papers
Digital Video Image Quality and Perceptual Coding (Signal Processing and Communications)
Digital Video Image Quality and Perceptual Coding (Signal Processing and Communications)
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Color to Gray: Visual Cue Preservation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting images by combining selected atlases on manifold
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Grassmann Hashing for approximate nearest neighbor search in high dimensional space
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Multispectral image visualization through first-order fusion
IEEE Transactions on Image Processing
Subspaces Indexing Model on Grassmann Manifold for Image Search
IEEE Transactions on Image Processing
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It is important to convert color images into grayscale ones for both commercial and scientific applications, such as reducing the publication cost and making the color blind people capture the visual content and semantics from color images. Recently, a dozen of algorithms have been developed for color-to-gray conversion. However, none of them considers the visual attention consistency between the color image and the converted grayscale one. Therefore, these methods may fail to convey important visual information from the original color image to the converted grayscale image. Inspired by the Helmholtz principle (Desolneux et al. 2008 [16]) that ''we immediately perceive whatever could not happen by chance'', we propose a new algorithm for color-to-gray to solve this problem. In particular, we first define the Chance of Happening (CoH) to measure the attentional level of each pixel in a color image. Afterward, natural image statistics are introduced to estimate the CoH of each pixel. In order to preserve the CoH of the color image in the converted grayscale image, we finally cast the color-to-gray to a supervised dimension reduction problem and present locally sliced inverse regression that can be efficiently solved by singular value decomposition. Experiments on both natural images and artificial pictures suggest (1) that the proposed approach makes the CoH of the color image and that of the converted grayscale image consistent and (2) the effectiveness and the efficiency of the proposed approach by comparing with representative baseline algorithms. In addition, it requires no human-computer interactions.