Unsupervised color-image segmentation by multicolor space iterative pixel classification

Vandenbroucke, Nicolas; Busin, Laurent; Macaire, Ludovic

doi:10.1117/1.jei.24.2.023032

Cited by 8 publications

(5 citation statements)

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“…For this reason, an alternative approach emerged: it consists of simultaneously exploiting the properties of several color spaces. Three main strategies are proposed in the literature: Color space fusion, which involves fusing the results from several classifiers, each one operating in a different color space [ 53 , 54 , 55 , 56 ], Color space selection, which consists of selecting the most well suited color spaces which are based on some specific quality criteria [ 57 , 58 , 59 , 60 ], Color texture feature selection that evaluates the texture features over different color spaces and selects the set of features that provide the best discrimination between the different textures classed by using a supervised feature selection approach [ 34 , 61 , 62 , 63 , 64 ]. …”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

“…Color space selection, which consists of selecting the most well suited color spaces which are based on some specific quality criteria [ 57 , 58 , 59 , 60 ],…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

“…As explained previously, there exist numerous color spaces. These color spaces take into account different physical, physiologic and psycho-visual properties, and can be classified into four families [ 60 ].

color spaces are here considered for experiments:

and

, which belong to the primary space family,

and

, which are luminance-chrominance spaces,

, which is an independent color component space, HSV , HSI , HLS and I-HLS , which belong to the perceptual space family.…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

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Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

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Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

“…Color space selection, which consists of selecting the most well suited color spaces which are based on some specific quality criteria [ 57 , 58 , 59 , 60 ],…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

color spaces are here considered for experiments:

and

, which belong to the primary space family,

and

, which are luminance-chrominance spaces,

, which is an independent color component space, HSV , HSI , HLS and I-HLS , which belong to the perceptual space family.…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

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Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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“…Color has traditionally been used as an important feature for image processing. Vandenbroucke et al 15,16 proposed image segmentation method using multiple color spaces. Vandenbroucke et al 15 used a hybrid color space that consists of 14 color representations in three channels and proposed a method to select the best color feature.…”

Section: Color Spacementioning

confidence: 99%

“…van Erp et al developed a method to select the optimal color space for image segmentation. Vandenbroucke et al perform segmentation by selecting an appropriate color space among multiple color spaces. In contrast, we propose a method to refine color of an object by using multiple color spaces concurrently.…”

Section: Related Workmentioning

confidence: 99%

Color refinement using deep neural networks for enhancing color recognition in a projector‐camera system

2019

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In projector‐camera systems, object recognition is essential to enable users to interact with physical objects. Among several input features used by the object classifier, color information is widely used as it is easily obtainable. However, the color of an object seen by the camera changes due to the projected light from the projector, which degrades the recognition performance. To solve this problem, we propose a method to restore the original color of an object from the observed color through camera. The color refinement method has been developed based on the deep neural network. The inputs to the neural network are the color of the projector light as well as the observed color of the object in multiple color spaces, including RGB, HSV, HIS, and HSL. The neural network is trained in a supervised manner. Through a number of experiments, we show that our refinement method reduces the difference from the original color and improves the object recognition rate implemented with a number of classification methods.

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Multi Color Channel vs. Multi Spectral Band Representations for Texture Classification

Vandenbroucke¹,

Porebski²

2021

Pattern Recognition. ICPR International Workshops and Challenges

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Unsupervised color-image segmentation by multicolor space iterative pixel classification

Abstract: International audienc

Cited by 8 publications

References 0 publications

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Color refinement using deep neural networks for enhancing color recognition in a projector‐camera system

Multi Color Channel vs. Multi Spectral Band Representations for Texture Classification

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