Applying incremental learning to parallel image segmentation

Cyril Charron, Yulia Hicks, Peter Hall

Research output: Chapter or section in a book/report/conference proceedingChapter or section

3 Citations (SciVal)

Abstract

Segmenting large or multiple images is time and memory consuming. These issues have been addressed in the past by implementing parallel versions of popular algorithms such as Graph Cuts and Mean Shift. Here, we propose to use an incremental Gaussian Mixture Model (GMM) learning algorithm for parallel image segmentation. We show that our approach allows us to reduce the memory requirements dramatically whilst obtaining high quality of segmentation. We also compare memory, time and quality of the performance of our approach and several other state of the art segmentation algorithms in a rigorous set of experiments, which produce favorable results.
Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
PublisherIEEE
Pages2064-2071
Number of pages8
ISBN (Print)9781424444427
DOIs
Publication statusPublished - 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, September 27, 2009 - October 4, 2009 - Kyoto, Japan
Duration: 1 Jan 2009 → …

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, September 27, 2009 - October 4, 2009
Country/TerritoryJapan
CityKyoto
Period1/01/09 → …

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