eprintid: 1795
rev_number: 8
eprint_status: archive
userid: 6
dir: disk0/00/00/17/95
datestamp: 2013-09-17 09:51:50
lastmod: 2013-09-17 10:36:05
status_changed: 2013-09-17 09:51:50
type: conference_item
metadata_visibility: show
creators_name: Minervini, Massimo
creators_name: Rusu, Cristian
creators_name: Tsaftaris, Sotirios A.
creators_id: massimo.minervini@imtlucca.it
creators_id: cristian.rusu@imtlucca.it
creators_id: sotirios.tsaftaris@imtlucca.it
title: Learning Computationally Efficient Approximations of Complex Image Segmentation Metrics
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
pres_type: paper
abstract: Image segmentation metrics have been extensively used in the literature to compare segmentation algorithms among each other, or relative to a ground-truth segmentation. Some metrics are easy to compute (e.g., Dice, Jaccard), others are more accurate (e.g., the Hausdorff distance) and may reflect local topology, but they are computationally demanding. While certain attempts have been made to create computationally efficient implementations of such complex metrics, in this paper we approach this problem from a radically different viewpoint. We construct approximations of a complex metric (e.g., the Hausdorff distance), combining a small number of computationally lightweight metrics in a linear regression model. We also consider feature selection, using sparsity inducing strategies, to restrict the number of metrics employed significantly, without penalizing the predictive power of the model. We demonstrate our methodology with image data from plant phenotyping experiments. We find that a linear model can effectively approximate the Hausdorff distance using even a few features. Our approach can find many applications, but is largely expected to benefit distributed sensing scenarios where the sensor has low computational capacity, whereas centralized processing units have higher computational capabilities.
date: 2013
date_type: published
event_title: 8th international symposium on Image and Signal Processing and Analysis (ISPA 2013)
event_location: Trieste, Italy
event_dates: September 4-6, 2013
event_type: conference
refereed: TRUE
related_url_url: http://www.isispa.org/
related_url_type: org
citation:   Minervini, Massimo and Rusu, Cristian and Tsaftaris, Sotirios A.  Learning Computationally Efficient Approximations of Complex Image Segmentation Metrics.  In: 8th international symposium on Image and Signal Processing and Analysis (ISPA 2013), September 4-6, 2013, Trieste, Italy         (2013)