Skip to main content

Unsupervised Image Segmentation by Bayesian Discriminator Starting with K-means Classifier

Buy Article:

$12.00 plus tax (Refund Policy)

Image segmentation is a first step to vision system and used as a pre-processing for many applications such as pattern recognition, image classification, picture coding or target tracking. In the previous papers, we reported an unsupervised image segmentation method based on Bayesian classifier and applied it to object-to-object color transformation. Although Bayesian decision rule is a robust tool to classify the objects statistically with the minimum error in average, it needs to preset some appropriate class centers before starting the classifier. The location of initial seed points much influences the segmentation accuracy. This paper discusses a better way to set the initial seeds and reports the Bayesian discriminator works better when coupled with k-means classifier for correcting the location of seed points. In addition, the paper introduces a new application of proposed model into scene color interchanges between segmented objects.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Publication date: 2004-01-01

More about this publication?
  • For more than 30 years, IS&T's series of digital printing conferences have been the leading forum for discussion of advances and new directions in 2D and 3D printing technologies. A comprehensive, industry-wide conference that brings together industry and academia, this meeting includes all aspects of the hardware, materials, software, images, and applications associated with digital printing systems?particularly those involved with additive manufacturing and fabrication?including bio-printing, printed electronics, page-wide, drop-on-demand, desktop and continuous ink jet, toner-based systems, and production digital printing, as well as the engineering capability, optimization, and science involved in these fields. In 2016, the conference changed its name formally to Printing for Fabrication to better reflect the content of the meeting and the evolving technology of printing.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Membership Information
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more