Fast automatic saliency map driven geometric active contour model for color object segmentation

Segmenting objects from color images to obtain useful information is a challenging research area recently. In this paper, a novel algorithm by combining a saliency map with an extension of a geometric active contour model is proposed to automatically segment the object of interest. The saliency map is first generated from the input image by a histogram based contrast method. The most salient regions are then detected as dominant parts of the object. After that, a contour is initialized using salient regions determined. Finally, by applying a geometric active contour model, the contour starts evolving iteratively to segment object boundaries. Experimental results attained on various natural scene images have shown that our proposed method is able to not only replace manual initialized contour and improve the accuracy, noise robustness of segmentation but converge to an optimal solution earlier than recent active contour models as well.