author = "Linares, Oscar Cuadros and Botelho, Glenda and Rodrigues, 
                         Francisco and Batista Neto, Jo{\~a}o",
          affiliation = "{University of S{\~a}o Paulo} and {Universidade Federal do 
                         Tocantins} and {University of S{\~a}o Paulo} and {University of 
                         S{\~a}o Paulo}",
                title = "An Adjustable Error Measure for Image Segmentation Evaluation",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "error measure, metric, evaluation, image segmentation.",
             abstract = "Due to the subjective nature of the segmentation process, 
                         quantitative evaluation of image segmentation methods is still a 
                         difficult task. Humans perceive image objects in different ways. 
                         Consequently, human segmentations may come in different levels of 
                         refinement, ie, under- and over-segmentations. Popular 
                         segmentation error measures in the literature (Arbelaez and OCE) 
                         are supervised methods (also called empirical discrepancy 
                         methods), in which error is computed by comparing objects in 
                         segmentations with a reference (ground-truth) image produced by 
                         humans. Since reference images can be many, the key issue for a 
                         segmentation error measure is to be consistent in the presence of 
                         both under- and over-segmentation. In general, the term 
                         consistency refers to the ability of the error measure to be low, 
                         when comparing similar segmentations, or high, when faced with 
                         different segmentations, while capturing under- or 
                         over-segmentations. In this paper we propose a new object-based 
                         empirical discrepancy error measure, called Adjustable Object- 
                         based Measure (AOM). We introduce a penalty parameter which gives 
                         the method the ability to be more (or less) responsive in the 
                         presence of over-segmentation. Hence, we extend the notion of 
                         consistency so as to include the applications need in the process. 
                         Some applications require segmentation to be extremely accurate, 
                         hence under- or over-segmentation should be well penalised. 
                         Others, do not. By changing the penalty parameter, AOM can deliver 
                         more consistent results not only in reference to the under- or 
                         over-segmentation issue alone, but also according to the nature of 
                         the application. We compare our method with Arbelaez (used as 
                         standard measure in the benchmark of Berkeley Segmentation Image 
                         Dataset) and OCE. Our results show that AOM not only is more 
                         consistent in the presence of over-segmentation, but is also 
                         faster to compute. Unlike Arbelaez and OCE, AOM also satisfies the 
                         metric axiom of symmetry.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3768029.pdf",
        urlaccessdate = "2021, Dec. 07"