Image-object extraction using a genetic-programming-based object model
Atsushi Shimizu, Shih‐Fu Chang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
This paper presents new algorithm for a person extraction system in video. Generally, segmentation schemes are based on some criteria related to homogeneous properties of image features, such as color and motion. However, typical semantic objects comprise multiple regions having different properties, and this severely affects the segmentation results. In this paper, we propose a method to extract the block-based boundaries of semantic objects as one of the key components of our system. The method relies on an idea of integrating the manipulations of image features at an initial level with no semantics (e.g., color) and an object model at a higher level with semantics. To do so, we use genetic programing (GP) to create the object model with a set of training images. A Maximum A Posteriori (MAP) estimation procedure is applied so that the object model and the image features are integrated. In a testing process, we fuse two segmentation results: the block-based contour extracted with the MAP procedure and arbitrary shaped regions obtained with a color segmentation scheme. Thus, the final contour of an object is acquired. The proposed algorithm is applied to extract the head and the body of a person in our experiment.