A Novel Histogram-Based Multi-Threshold Searching Algorithm for Multilevel Colour Thresholding

Image segmentation is an important preliminary process required in object tracking applications. This paper addresses the issue of unsupervised multi-colour thresholding design for colour-based multiple objects segmentation. Most of the current unsupervised colour thresholding techniques require adopting a supervised training algorithm or a cluster-number decision algorithm to obtain optimal threshold values of each colour channel for a colour-of-interest. In this paper, a novel unsupervised multi-threshold searching algorithm is proposed to automatically search the optimal threshold values for segmenting multiple colour objects. To achieve this, a novel ratio-map image computation method is proposed to efficiently enhance the contrast between colour and non-colour pixels. The Otsu's method is then applied to the ratio-map image to extract all colour objects from the image. Finally, a new histogram-based multi-threshold searching algorithm is developed to search the optimal upper-bound and lower-bound threshold values of hue, saturation and brightness components for each colour object. Experimental results show that the proposed method not only succeeds in separating all colour objects-of-interest in colour images, but also provides satisfactory colour thresholding results compared with an existing multilevel thresholding method.