A new online learning algorithm with application to image segmentation

Mingkun Li; Ishwar K. Sethi

doi:10.1117/12.586328

1 March 2005 A new online learning algorithm with application to image segmentation

Mingkun Li, Ishwar K. Sethi

Proceedings Volume 5672, Image Processing: Algorithms and Systems IV; (2005) https://doi.org/10.1117/12.586328
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

In this paper we present a new online learning and classification algorithm and suggest its use for image segmentation. Our learning algorithm follows a variation of Bayesian estimation procedure, which combines prior knowledge and knowledge learned from data. Our classification algorithm strictly follows a statistical classification procedure. The new online learning algorithm is simple to implement, robust to initial parameters and has a linear complexity. The experimental results using computer generated data show that the proposed online learning algorithm can quickly learn the underlying structure from data. The proposed online learning algorithm is used to develop a novel image segmentation procedure. This image segmentation procedure is based on the region growing and merging approach. First, region growing is carried out using the online learning algorithm. Then, a merging operation is performed to merge the small regions. Two merging methods are proposed. The first method is based on statistical similarity and merges the statistically similar and spatially adjacent regions. The second method uses an information-based approach merging small regions into their neighbouring larger regions. Many experimental results clearly show the efficacy of the proposed image segmentation method.

Citation Download Citation

Mingkun Li and Ishwar K. Sethi "A new online learning algorithm with application to image segmentation", Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); https://doi.org/10.1117/12.586328

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available