Shape sharing for object segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Object co-segmentation via discriminative low rank matrix recovery
Proceedings of the 21st ACM international conference on Multimedia
PatchNet: a patch-based image representation for interactive library-driven image editing
ACM Transactions on Graphics (TOG)
Object class detection: A survey
ACM Computing Surveys (CSUR)
SalientShape: group saliency in image collections
The Visual Computer: International Journal of Computer Graphics
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We present a novel technique for figure-ground segmentation, where the goal is to separate all foreground objects in a test image from the background. We decompose the test image and all images in a supervised training set into overlapping windows likely to cover foreground objects. The key idea is to transfer segmentation masks from training windows that are visually similar to windows in the test image. These transferred masks are then used to derive the unary potentials of a binary, pairwise energy function defined over the pixels of the test image, which is minimized with standard graph-cuts. This results in a fully automatic segmentation scheme, as opposed to interactive techniques based on similar energy functions. Using windows as support regions for transfer efficiently exploits the training data, as the test image does not need to be globally similar to a training image for the method to work. This enables to compose novel scenes using local parts of training images. Our approach obtains very competitive results on three datasets (PASCAL VOC 2010 segmentation challenge, Weizmann horses, Graz-02).