Abstract

In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of ob- jects within a general class (such as horse images). The approach is dif- ferent from bottom-up segmentation methods that primarily use the con- tinuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined. Keywords: Grouping and segmentation; Figure-ground; Top-down pro- cessing; Ob ject classification