Video Object Segmentation Using Color-Component-Selectable Learning for Self-Organizing Maps

In this paper, 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 probability density function is calculated for each component value according to kernel estimation at the first fame. Some areas randomly chosen from the background are then examined, using each component value, whether it is misjudged that they include the target object. The quantification is determined so that occurrence frequency of the above false extraction can be reduced. The data presented to maps are generated, based on the selected quantification. Experimental results show that the proposed method well recognizes the target object.