Comparison of perceptual color spaces for natural image segmentation tasks

Correa-Tome, Fernando E.

doi:10.1117/1.3651799

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…A comparative study between several perceptually uniform color spaces (specifically: L * a * b *, L * u * v * and Rlab) are presented in order to establish which color space is better for the segmentation of natural images. To perform the comparison, an empirical discrepancy method is used.…”

Section: Previous Workmentioning

confidence: 99%

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

Alvarado-Cervantes

Felipe-Riveron

Khartchenko

et al. 2016

Color Research & Application

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In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

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Section: Previous Workmentioning

confidence: 99%

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

Alvarado-Cervantes

Felipe-Riveron

Khartchenko

et al. 2016

Color Research & Application

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“…The third way is applying polymerase chain reaction (Henson and French, 1993;Schaad et al 2002; Koo et al, 2013) [3][4][5] by biological operation; however, the experimental procedure is complicated for ordinary farmers. With the development of computer vision, another way is image-based recognition of plant disease, which is proposed and applied widely [6][7][8][9][10][11][12][13][14] . Bo Li et al,2015 [15] proposed a shallow artificial neural network model to analyse images of cherry and plum shoots.…”

Section: Introductionmentioning

confidence: 99%

L2MXception: An Improved Xception Network for Classification of Peach diseases 

Yao

Ni²,

Wang

et al. 2020

Preprint

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Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

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“…31 In the context of image segmentation, it is known that the use of a given color representation has a relevant impact on the performance of a segmentation method. 24 Different studies have been oriented to the determination of the best suited color representation for a given segmentation approach [25][26][27][28] and have concluded that the socalled perceptual color spaces provide better results. In this regard, and for our specific method, we explore the use of different color spaces and their impacts in the performance results.…”

Section: Definition Of the Color Spaces Under Analysismentioning

confidence: 99%

“…24 Different studies have tried to determine the best-suited color representation for a given segmentation approach. [25][26][27][28] Some of them have found that the so-called perceptual color spaces, e.g., CIELab and CIELuv, are the most appropriate when the resemblance to the human visual system is desirable. The main advantage of these color representations is that the Euclidean distance between two points is approximately proportional to the perceptual difference between the two colors represented by those points.…”

Section: Introductionmentioning

confidence: 99%

Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose a color image segmentation approach based on rough set theory elements. Main contributions of the proposed approach are twofold. First, by using an adaptive threshold selection, the approach is automatically adjustable according to the image content. Second, a region-merging process, which takes into account both features and spatial relations of the resulting segments, lets us minimize over-segmentation issues. These two proposals allow our method to overcome some performance issues shown by previous rough set theory-based approaches. In addition, a study to determine the best suited color representation for our segmentation approach is carried out, determining that the best results are obtained using a perceptually uniform color space. A set of qualitative and quantitative tests over a comprehensive image database shows that the proposed method produces high-quality segmentation outcomes, better than those obtained using the previous rough set theory-based and standard segmentation approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Comparison of perceptual color spaces for natural image segmentation tasks

Cited by 17 publications

References 21 publications

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

L2MXception: An Improved Xception Network for Classification of Peach diseases

Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces

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Comparison of perceptual color spaces for natural image segmentation tasks

Cited by 17 publications

References 21 publications

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

L2MXception: An Improved Xception Network for Classification of Peach diseases&nbsp;

Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces

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L2MXception: An Improved Xception Network for Classification of Peach diseases