Video object segmentation using color-component-selectable learning for self-organizing maps

In this article, self-organizing-map-based video object segmentation is proposed, assuming that either Y-quantification or HSV-quantification can be systematically selected. Given a video sequence, the value of the probability density function for each component value is calculated according to a kernel estimation at the first frame. Some areas randomly chosen from the background are then examined, using each component value, to judge whether or not they include the target object. The quantification is determined so that the frequency of occurrence of false extractions can be reduced. The data presented to the maps are generated based on the selected quantification. Experimental results show that the proposed method recognizes the target object well.