Anucha Tungkatsathan, W. Premchaiswadi, Nucharee Premchaiswadi
{"title":"Unsupervised Detection for Minimizing a Region of Interest around Distinct Object in Natural Images","authors":"Anucha Tungkatsathan, W. Premchaiswadi, Nucharee Premchaiswadi","doi":"10.1109/DICTA.2010.45","DOIUrl":null,"url":null,"abstract":"One of the major challenges for region-based image retrieval is to identify the Region of Interest (ROI) that comprises object queries. However, automatically identifying the regions or objects of interest in a natural scene is a very difficult task because the content is complex and can be any shape. In this paper, we present a novel unsupervised detection method to automatically and efficiently minimize the ROI in the images. We applied an edge-based active contour model that drew upon edge information in local regions. The mathematical implementation of the proposed active contour model was accomplished using a variational level set formulation. In addition, the mean-shift algorithm was used to reduce the sensitivity of parameter change of level set formulation. The results show that our method can overcome the difficulties of non-uniform sub-region and intensity in homogeneities in natural image segmentation.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Digital Image Computing: Techniques and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA.2010.45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
One of the major challenges for region-based image retrieval is to identify the Region of Interest (ROI) that comprises object queries. However, automatically identifying the regions or objects of interest in a natural scene is a very difficult task because the content is complex and can be any shape. In this paper, we present a novel unsupervised detection method to automatically and efficiently minimize the ROI in the images. We applied an edge-based active contour model that drew upon edge information in local regions. The mathematical implementation of the proposed active contour model was accomplished using a variational level set formulation. In addition, the mean-shift algorithm was used to reduce the sensitivity of parameter change of level set formulation. The results show that our method can overcome the difficulties of non-uniform sub-region and intensity in homogeneities in natural image segmentation.