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@Article{CarvalhoManSobComWan:2015:ImGrIm,
               author = "Carvalho, L. E. and Mantelli Neto, Sylvio Luiz and Sobieranski, A. 
                         C. and Comunello, Eros and von Wangenheim, A.",
          affiliation = "{Universidade Federal de Santa Catarina (UFSC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Santa Catarina (UFSC)} and {Universidade Federal de Santa 
                         Catarina (UFSC)} and {Universidade Federal de Santa Catarina 
                         (UFSC)}",
                title = "Improving graph-based image segmentation using non-linear color 
                         similarity metrics",
              journal = "International Journal of Image and Graphics",
                 year = "2015",
               volume = "16",
               number = "21",
                month = "June",
             keywords = "Felzenszwalb and Huttenlocher method, Polynomial Mahalanobis 
                         Distance, Non-Linear Color Similarity Metrics.",
             abstract = "We present a new segmentation method called Weighted Felzenszwalb 
                         and Hutten-locher method (WFH), an improved version of the 
                         well-known graph-based segmentation method, Felzenszwalb and 
                         Huttenlocher method (FH). Our method uses a non-linear 
                         discrimination function based on Polynomial Mahalanobis Distance 
                         (PMD) as the color similarity metric. Two empirical validation 
                         experiments were performed using as a golden standard ground 
                         truths from a publicly available source, the Berkeley dataset, and 
                         an objective segmentation quality measure, the Rand dissimilarity 
                         index. In the first experiment the results were compared against 
                         the original FH method. In the second, WFH was compared against 
                         several well-known segmentation methods. In both cases WFH 
                         presented significant better similarity results when compared with 
                         the golden standard and segmentation results presented a reduction 
                         of over-segmented regions.",
                 issn = "0219-4678",
             language = "en",
           targetfile = "carvalho_improving.pdf",
        urlaccessdate = "23 nov. 2020"
}


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