author = "Fernandez, Mariela Atausinchi and Lopes, Rubens M. and Hirata, 
                         Nina S. T.",
          affiliation = "{Institute of Mathematics and Statistics - University of S{\~a}o 
                         Paulo} and {Oceanographic Institute - University of S{\~a}o 
                         Paulo} and {Institute of Mathematics and Statistics - University 
                         of S{\~a}o Paulo}",
                title = "Image Segmentation Assessment from the Perspective of a Higher 
                         Level Task",
            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 = "holistic system evaluation, classification evaluation, plankton 
                         image classification, segmentation evaluation, plankton image 
             abstract = "Image segmentation evaluation is usually performed by visual 
                         inspection, by comparing segmentation to a ground-truth, or by 
                         computing an objective function value for the segmented image. All 
                         these methods require user participation either for manual 
                         evaluation, or to define ground-truth, or to embed desired 
                         segmentation properties into the objective function. However, 
                         evaluating segmentation is a hard task if none of these three 
                         methods can be easily employed. Often, higher level tasks such as 
                         detecting or classifying objects can be performed much more easily 
                         than low level tasks such as delineating the contours of the 
                         objects. This fact can be advantageously used to evaluate 
                         algorithms for a low level task. We apply this approach to a case 
                         study on plankton classification. Segmentation methods are 
                         evaluated from the perspective of plankton classification 
                         accuracy. This approach not only helps choosing a good 
                         segmentation method but also helps detecting points where 
                         segmentation is failing. In addition, this more holistic form of 
                         segmentation evaluation better meets requirements of big data 
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3769249.pdf",
        urlaccessdate = "2021, Dec. 04"