Segmenting an image into semantically meaningful parts is a fundamental and challenging task in computer vision. Automatic methods are able to segment an image into coherent regions, but such regions generally do not correspond to complete meaningful parts. In this paper, we show that even a single training example can greatly facilitate the induction of a semantically meaningful segmentation on novel images within the same domain: images depicting the same, or similar, objects in a similar setting. Our approach constructs a non-parametric representation of the example segmentation by selecting patch-based representatives. This allows us to represent complex semantic regions containing a large variety of colors and textures. Given an input image, we first partition it into small homogeneous fragments, and the possible labelings of each fragment are assessed using a robust voting procedure. Graph-cuts optimization is then used to label each fragment in a globally optimal manner.
|Original language||American English|
|Number of pages||12|
|Journal||Lecture Notes in Computer Science|
|State||Published - 2006|
|Event||7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India|
Duration: 13 Jan 2006 → 16 Jan 2006