extracting detected salient object by active segmentation
Thanks to many state-of-the-art researches on visual attention techniques, we have ability to locate or focus on salient objects automatically, which are usually the main characters of the scene. To robustly extract the salient object, this paper suggests a combination of visual attention techniques and active segmentation. Active segmentation has introduced an innovative idea about inside-out segmentation but we have to manually specify the initial fixations to make the algorithm work. So, visual attention techniques are used to grant active segmentation the automatic ability. To that purpose, this work also introduces a local voting method to identify the ideal fixation points and merging rules for an optimal presentation of output. The detection and segmentation results show that the proposed method can automatically detect and segment salient objects robustly while it also can totally avoid the appearance of scatter segments, which frequently occur due to segmentation with binarized saliency map. The performance of our proposed combination model on public salient object database outperforms the other tested methods in terms of precision, recall and F-measure.
visual attention local voting auto-segmentation neighbor support object detection