tailieunhanh - Báo cáo hóa học: " Distance Measures for Image Segmentation Evaluation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Distance Measures for Image Segmentation Evaluation | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 35909 Pages 1-10 DOI ASP 2006 35909 Distance Measures for Image Segmentation Evaluation Xiaoyi Jiang 1 Cyril Marti 2 Christophe Irniger 2 and Horst Bunke2 1 Computer Vision and Pattern Recognition Group Department of Computer Science University of Munster Einsteinstrasse 62 D-48149 Munster Germany 2 Institute of Computer Science and Applied Mathematics University of Bern Neubruckstrasse 10 CH-3012 Bern Switzerland Received 17 March 2005 Revised 10 July 2005 Accepted 31 July 2005 The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and as a consequence to use measures for comparing clusterings developed in statistics and machine learning. By doing so we obtain a variety of performance measures which have not been used before in image processing. In particular some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Image segmentation and recognition are central problems of image processing for which we do not yet have any general purpose solution approaching human-level competence. Recognition is basically a classification task and one can empirically estimate the recognition performance probability of misclassification by counting classification errors on a test set. Today reporting recognition performance on large data sets is a well-accepted standard. In contrast segmentation performance evaluation remains subjective. Typically results on a few images are shown and the authors argue why they .

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