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. 2020 Jan 10;15(1):e0226345.
doi: 10.1371/journal.pone.0226345. eCollection 2020.

Color image segmentation using adaptive hierarchical-histogram thresholding

Min Li  1   2 , Lei Wang  1   2 , Shaobo Deng  1   2 , Chunhua Zhou  3
Affiliations

Color image segmentation using adaptive hierarchical-histogram thresholding

Min Li et al. PLoS One. .

Abstract

Histogram-based thresholding is one of the widely applied techniques for conducting color image segmentation. The key to such techniques is the selection of a set of thresholds that can discriminate objects and background pixels. Many thresholding techniques have been proposed that use the shape information of histograms and identify the optimum thresholds at valleys. In this work, we introduce the novel concept of a hierarchical-histogram, which corresponds to a multigranularity abstraction of the color image. Based on this, we present a new histogram thresholding-Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm, which can adaptively identify the thresholds from valleys. The experimental results have demonstrated that the AHHT algorithm can obtain better segmentation results compared with the histon-based and the roughness-index-based techniques with drastically reduced time complexity.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of the histogram-based technique.
Fig 2
Fig 2. Hierarchical-histogram of each plane of R, G, and B of the image Moon obtained by AHHT.
Fig 3
Fig 3. The image Moon: (a) original image, (b) initial segmented result (225 colors), (c) final segmented result (4 colors).
Fig 4
Fig 4. The image Birds: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
Fig 5
Fig 5. The image Church: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
Fig 6
Fig 6. The image Mountain: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
Fig 7
Fig 7. The image Marsh: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
Fig 8
Fig 8. The image Boating: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
Fig 9
Fig 9. The image Snake: (a) original image, (b, c) initial segmented result and result after region merging based on the histon, (d, e) initial segmented result and result after region merging based on the roughness index, (f, g) initial segmented result and result after region merging based on AHHT.
See this image and copyright information in PMC

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