Title: Learning from large dataset: segmentation of capsule endoscopy videos

Authors: Xiaohui Yuan; Balathasan Giritharan; Sandeep Panchakarla

Addresses: University of North Texas, Denton TX 76201, USA ' University of North Texas, Denton TX 76201, USA ' University of North Texas, Denton TX 76201, USA

Abstract: Reviewing video of capsule endoscopy is a tedious work that takes hours. Hence, efficient and scalable approaches are needed to automate the process of large dataset and be able to refine the model given new examples. This paper presents an incremental SVM to learn from large dataset with dynamic patterns. Our method extends the reduced convex hull concept and defines the approximate skin segments of convex hulls. Experiments were conducted using synthetic data set, real-world data sets, and CE videos. Our results demonstrated highly competitive performance that requires much less resource, which cast new light on learning with limited resource.

Keywords: classification; video segmentation; incremental learning; large datasets; capsule endoscopy videos; SVM; support vector machines; approximate skin segments; convex hulls.

DOI: 10.1504/IJFIPM.2012.050417

International Journal of Functional Informatics and Personalised Medicine, 2012 Vol.4 No.1, pp.1 - 14

Received: 10 May 2011
Accepted: 12 Jun 2011

Published online: 20 Nov 2012 *

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