Adaptive unsupervised multi-view feature selection for visual concept recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
Computer Vision and Image Understanding
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For the interpretation of a visual scene, it is important for a robotic system to pay attention to the objects in the scene and segment them from their background. We focus on the segmentation of previously unseen objects in unknown scenes. The attention model therefore needs to be bottom-up and context-free. In this paper, we propose the use of symmetry, one of the Gestalt principles for figure-ground segregation, to guide the robot's attention. We show that our symmetry-saliency model outperforms the contrast-saliency model, proposed in (Itti et al 1998). The symmetry model performs better in finding the objects of interest and selects a fixation point closer to the center of the object. Moreover, the objects are better segmented from the background when the initial points are selected on the basis of symmetry.