@InProceedings{LinaresBoteRodrBati:2015:AdErMe,
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, BA, Brazil",
conference-year = "26-29 Aug. 2015",
doi = "10.1109/SIBGRAPI.2015.29",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.29",
language = "en",
ibi = "8JMKD3MGPBW34M/3JNHU48",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JNHU48",
targetfile = "PID3768029.pdf",
urlaccessdate = "2024, Apr. 28"
}