Entropy-based circular histogram thresholding for color image segmentation

Kang, Chao; Wu, Chengmao; Fan, Jiulun

doi:10.1007/s11760-020-01723-2

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…It is important to emphasize that the idea of mean-based breakpoint selection criterion is different from the existing Lorenz curve-based and cumulative distribution entropybased breakpoint selection criteria [20,21]. e mean-based method uses the invariants of circular statistics and linear statistics.…”

Section: Breakpoint Selection Criteriamentioning

confidence: 99%

“…To further illustrate the scope and effect of the mean-based breaking method, the H component circular histogram corresponding to the 8 color images in the Berkeley dataset is selected for breakpoint selection and compared with the Lorenz-based [20], CDFEbased [21], and ABC-based [22] breakpoint selection criteria. e linearization result of 8 images can be seen in Figures 10-17.…”

Section: Real Circular Histogramsmentioning

confidence: 99%

“…One is the criterion based on the Lorenz curve [20]; we discussed the relation between the area difference and the expansion direction and gave the optimal breakpoint selection criterion in the anticlockwise or clockwise direction. e other is the criterion based on the cumulative distribution entropy [21], and we built a circular histogram expansion model based on the cumulative distribution entropy and discussed the optimal breakpoint selection criteria under different expansion directions.…”

Section: Introductionmentioning

confidence: 99%

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Mean-Based Breakpoint Selection on Circular Histogram

Fan

Yang

2021

Mathematical Problems in Engineering

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Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

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Section: Breakpoint Selection Criteriamentioning

confidence: 99%

Section: Real Circular Histogramsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mean-Based Breakpoint Selection on Circular Histogram

Fan

Yang

2021

Mathematical Problems in Engineering

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“…Recently, Kang et al [13,14] proposed two ways on circular histogram thresholding based on the maximum entropy principle. In the first way, the circular histogram was converted into a linear histogram at the appropriate breakpoint using Lorenz curve, and then the traditional maximum entropy thresholding method was used to segment.…”

Section: Introductionmentioning

confidence: 99%

“…It can be found from the literature [13,14] that the entropy-based method on circular histogram may be much better than the Otsu-based method. However, separating the circular histogram into two classes needs two thresholds, so it takes O(L 2 ) time to use maximum entropy thresholding on circular histogram.…”

Section: Introductionmentioning

confidence: 99%

Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram

Yang

Fan

Wang

2021

Mathematical Problems in Engineering

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Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

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