Learning Object Shape: From Drawings to Images

We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of "outlining" an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for hand-segmented training images. In this paper we present a novel landmark-based, piecewise-linear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for hand-segmentation by transferring the shape "essence" of an object from drawings to complex images. We show that our method is able to automatically and effectively learn and localize a variety of object classes.

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Gal Elidan, Geremy Heitz, Daphne Koller
• Keywords: Computer Vision, CVPR 2006, Deformable Object Classes, Hand-segmented Training Images, Major Obstacle, Minimal User Supervision