Image-object extraction using a genetic-programming-based object model

Atsushi Shimizu; Shih-Fu Chang

doi:10.1117/12.382978

19 April 2000 Image-object extraction using a genetic-programming-based object model

Atsushi Shimizu, Shih-Fu Chang

Proceedings Volume 3974, Image and Video Communications and Processing 2000; (2000) https://doi.org/10.1117/12.382978
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Atsushi Shimizu and Shih-Fu Chang "Image-object extraction using a genetic-programming-based object model", Proc. SPIE 3974, Image and Video Communications and Processing 2000, (19 April 2000); https://doi.org/10.1117/12.382978

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