A Perceptual Comparison of Distance Measures for Color Constancy Algorithms (original) (raw)
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Perceptual analysis of distance measures for color constancy algorithms
Journal of Visual Communication and Image Representation, 2009
Color constancy algorithms are often evaluated by using a distance measure that is based on mathematical principles, such as the angular error. However, it is unknown whether these distance measures correlate to human vision. Therefore, the main goal of our paper is to analyze the correlation between several performance measures and the quality, obtained by using psychophysical experiments, of the output images generated by various color constancy algorithms. Subsequent issues that are addressed are the distribution of performance measures, suggesting additional and alternative information that can be provided to summarize the performance over a large set of images, and the perceptual significance of obtained improvements, i.e., the improvement that should be obtained before the difference becomes noticeable to a human observer.
A Perceptual Analysis of Distance Measures for Color Constancy Algorithms
Journal of the Optical Society of America a, 2009
Color constancy algorithms are often evaluated by using a distance measure that is based on mathematical principles, such as the angular error. However, it is unknown whether these distance measures correlate to human vision. Therefore, the main goal of our paper is to analyze the correlation between several performance measures and the quality, obtained by using psychophysical experiments, of the output images generated by various color constancy algorithms. Subsequent issues that are addressed are the distribution of performance measures, suggesting additional and alternative information that can be provided to summarize the performance over a large set of images, and the perceptual significance of obtained improvements, i.e., the improvement that should be obtained before the difference becomes noticeable to a human observer.
Comparing objective and subjective error measures for color constancy
Environmental Pollution, 2008
We compare an objective and a subjective performance measure for color constancy algorithms. Eight hyper-spectral images were rendered under a neutral reference illuminant and four chromatic illuminants (Red, Green, Yellow, Blue). The scenes rendered under the chromatic illuminants were color corrected by 5 color constancy algorithms that are based on zero-, first-and second-order image statistics. The angular error is used as the objective performance measure for color constancy. It estimates the chromatic mismatch between the true and estimated illuminant vector in RGB space. A subjective performance measure was derived from a psychophysical experiment involving paired comparisons of the color corrected images shown on a calibrated monitor. Eight subjects indicated their preference with respect to color reproduction when comparing the two images (i.e. color constancy algorithms) against the reference image (the same scene under neutral illumination). Our results indicate a large negative correlation (-0.9 on average) between the objective and subjective color constancy measures. The data suggests the possibility for further improvement of the correlation between the two types of performance measures.
Evaluation of Color Constancy Algorithms
This paper presents a review on various color constancy (CC) techniques. The CC is a method that gathers the effect of various light sources on a digital image. The scene recorded by a camera relies on three issues: the physical information of the object, the illumination incident on the scene, and the characteristics of the camera. The objective of CC is to account for the effect of the illuminate. Many existing methods such as Grey-world method, Physics based CC and Edge-based methods were used to measure the CC of objects affected by different light source. All these methods have obvious limitations that the light source across the scene is spectrally uniform. This assumption is often violated as there might be more than one light source illuminating the scene. The overall objective of this paper is to find the gaps in earlier work on CC.
Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset
Journal of Imaging Science and Technology, 2009
The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper the authors propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral gray sphere, which better copes with the illuminant variations of the scene.
A comparison of computational color constancy Algorithms. II. Experiments with image data
IEEE Transactions on Image Processing, 2002
We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely.
Ieee Transactions on Image Processing, 2002
We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely.
Automatic color constancy algorithm selection and combination
Pattern Recognition, 2010
In this work, we investigate how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images. We show how these properties can be used to drive, given a set of illuminant estimation algorithms, the selection of the best algorithm for a given image. The algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features. The features represent the image content in terms of low-level visual properties. The trees are trained to select the algorithm that minimizes the expected error in illuminant estimation. We also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms' estimations. Experimental results on the widely used Ciurea and Funt dataset demonstrate the effectiveness of our approach.
A Novel Colour-Constancy Algorithm: A Mixture of Existing Algorithms
2012
Colour constancy algorithms attempt to provide an accurate colour representation of images independent of the illuminant colour used for scene illumination. In this paper we investigate well-known and state-ofthe-art colour-constancy algorithms. We then select a few of these algorithms and combine them using a weighted-sum approach. Four methods are involved in the weights estimation. The first method uniformly distributes the weights among the algorithms. The second one uses learning set of images to train the weights based on errors. The third method searches for a linear combination of all methods' outcomes that minimise the error. The fourth one trains a continuous perceptron, in order to find optimum combination of the methods. In all four approaches, we used a set of 60 images. Each of these images was taken with a Gretag Macbeth colour checker card in the scene, in order to make quantitative evaluation of colour-consistency algorithms. The results obtained show our proposed method outperforms individual algorithms. The best results were obtained using the weights for linear combination and the trained continuous perceptron to combine the algorithms.