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Proceedings Paper

A new online learning algorithm with application to image segmentation
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Paper 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.

Paper Details

Date Published: 1 March 2005
PDF: 10 pages
Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); doi: 10.1117/12.586328
Show Author Affiliations
Mingkun Li, Oakland Univ. (United States)
Ishwar K. Sethi, Oakland Univ. (United States)

Published in SPIE Proceedings Vol. 5672:
Image Processing: Algorithms and Systems IV
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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