Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach.
Description
Evaluating the application of semantic inferencing rules to image annotation
%0 Conference Paper
%1 Hollink2005Evaluating
%A Hollink, L.
%A Little, S.
%A Hunter, J.
%B K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture
%C New York, NY, USA
%D 2005
%I ACM
%K phd thesis-semantic-multimedia
%P 91--98
%R http://doi.acm.org/10.1145/1088622.1088639
%T Evaluating the application of semantic inferencing rules to image annotation
%U http://portal.acm.org/citation.cfm?id=1088639
%X Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach.
%@ 1-59593-163-5
@inproceedings{Hollink2005Evaluating,
abstract = {Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach.},
added-at = {2009-08-11T11:00:38.000+0200},
address = {New York, NY, USA},
author = {Hollink, L. and Little, S. and Hunter, J.},
biburl = {https://www.bibsonomy.org/bibtex/26a354309ae38c87a2209a090a3766c36/casi},
booktitle = {K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture},
description = {Evaluating the application of semantic inferencing rules to image annotation},
doi = {http://doi.acm.org/10.1145/1088622.1088639},
interhash = {cc2169524635055710914e0b2b9d51f7},
intrahash = {6a354309ae38c87a2209a090a3766c36},
isbn = {1-59593-163-5},
keywords = {phd thesis-semantic-multimedia},
location = {Banff, Alberta, Canada},
pages = {91--98},
publisher = {ACM},
timestamp = {2009-08-13T17:58:12.000+0200},
title = {Evaluating the application of semantic inferencing rules to image annotation},
url = {http://portal.acm.org/citation.cfm?id=1088639},
year = 2005
}