Image Segmentation via Normalised Cuts and Clustering Algorithm

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dc.contributor.authorMei, Yeen Choong
dc.contributor.authorWei, Yeang Kow
dc.contributor.authorYit, Kwong Chin
dc.contributor.authorLorita Angeline
dc.contributor.authorTze, Kin Teo (Kenneth)
dc.date.accessioned2023-06-06T06:25:58Z
dc.date.available2023-06-06T06:25:58Z
dc.date.issued2012
dc.descriptionModelling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Malaysia
dc.description.abstractImage segmentation has been widely applied in image analysis for various areas such as biomedical imaging, intelligent transportation systems and satellite imaging. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. Homogeneous regions of an image are regions containing common characteristics and are grouped as single segment. One of the graph partitioning methods in image segmentation, normalised cuts, has been recognised producing reliable segmentation result. To date, normalised cuts in image segmentation of various sized images is still lacking of analysis of its performance. In this paper, segmentation on synthetic images and natural images are covered to study the performance and effect of different image complexity towards segmentation process. This study gives some research findings for effective image segmentation using graph partitioning method with computation cost reduced. Because of its cost expensive and it becomes unfavourable in performing image segmentation on high resolution image especially in online image retrieval systems. Thus, a graph-based image segmentation method done in multistage approach is introduced here.
dc.identifier.urihttps://digitallibrary.peninsulacollege.edu.my/handle/123456789/328
dc.language.isoen
dc.titleImage Segmentation via Normalised Cuts and Clustering Algorithm
dc.typeArticle
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