Visual Saliency Based Multiple Objects Segmentation and its Parallel Implementation for Real-Time Vision Processing

摘要

This paper presents a segmentation method of multiple object regions based on visual saliency. Our method comprises three steps. First, attentional points are detected using saliency maps (SMs). Subsequently, regions of interest (RoIs) are extracted using scale-invariant feature transform (SIFT). Finally, foreground regions are extracted as object regions using GrabCut. Using RoIs as teaching signals, our method achieved automatic segmentation of multiple objects without learning in advance. As experimentally obtained results obtained using PASCAL2011 dataset, attentional points were extracted correctly from 18 images for two objects and from 25 images for single objects. We obtained segmentation accuracies: 64.1%, precision; 62.1%, recall, and 57.4%, F-measure. For real-time video image processing, we implemented our model on an IMAPCAR2 evaluation board. The processing cost was 47.5 ms for the video images of 640 × 240 pixel resolution. Moreover, we applied our method to time-series images obtained using a mobile robot. Attentional points were extracted correctly for seven images for two objects and three images for single objects from ten images. We obtained segmentation accuracies of 58.0%, precision; 63.1%, recall, and 58.1%, F-measure.