Combined Top-Down/Bottom-Up Segmentation

We construct an image segmentation scheme that combines top-down (TD) with bottom-up (BU) processing. In the proposed scheme, segmentation and recognition are intertwined rather than proceeding in a serial manner. The TD part applies stored knowledge about object shapes acquired through learning, whereas the BU part creates a hierarchy of segmented regions based on uniformity criteria. Beginning with unsegmented training examples of class and nonclass images, the algorithm constructs a bank of class-specific fragments and determines their figure-ground segmentation. This fragment bank is then used to segment novel images in a TD manner: The stored fragments are first used to recognize images containing class objects and then to create a complete cover that best approximates these objects. The resulting TD segmentation is then integrated with BU multiscale grouping to better delineate the object boundaries. Our experiments, applied to a large set of four classes (horses, pedestrians, ca...

• Type: Journal
• Year: 2008
• Where: PAMI
• Authors: Eran Borenstein, Shimon Ullman
• Keywords: Image Segmentation Scheme, Images, PAMI 2008, TD Segmentation