Image classification using neural networks and ontologies
C. Breen, L. Khan, and A. Ponnusamy. Database and Expert Systems Applications, 2002. Proceedings. 13th
International Workshop on, page 98--102. (2002)
Abstract
The advent of extremely powerful home PC and the growth of the Internet
have made the appearance of multimedia documents a common sight in
the computer world. In the world of unstructured data composed of
images and other media types, classification often comes at the price
of countless hours of manual labor. This research aims to present
a scalable system capable of examining images and accurately classifying
the image based on its visual content. When retrieving images based
on a user's query, the system yields a minimal amount of irrelevant
information (high precision) and ensures a maximum amount of relevant
information (high recall).
%0 Conference Paper
%1 Breen2002
%A Breen, C.
%A Khan, L.
%A Ponnusamy, A.
%B Database and Expert Systems Applications, 2002. Proceedings. 13th
International Workshop on
%D 2002
%K Internet, classification, content content-based data, databases, documents, feedback, image information, meta multimedia nets, networks, neural ontologies, precision, query, relevance relevant retrieval, scalable semantic system, user visual
%P 98--102
%T Image classification using neural networks and ontologies
%X The advent of extremely powerful home PC and the growth of the Internet
have made the appearance of multimedia documents a common sight in
the computer world. In the world of unstructured data composed of
images and other media types, classification often comes at the price
of countless hours of manual labor. This research aims to present
a scalable system capable of examining images and accurately classifying
the image based on its visual content. When retrieving images based
on a user's query, the system yields a minimal amount of irrelevant
information (high precision) and ensures a maximum amount of relevant
information (high recall).
@inproceedings{Breen2002,
abstract = {The advent of extremely powerful home PC and the growth of the Internet
have made the appearance of multimedia documents a common sight in
the computer world. In the world of unstructured data composed of
images and other media types, classification often comes at the price
of countless hours of manual labor. This research aims to present
a scalable system capable of examining images and accurately classifying
the image based on its visual content. When retrieving images based
on a user's query, the system yields a minimal amount of irrelevant
information (high precision) and ensures a maximum amount of relevant
information (high recall).},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Breen, C. and Khan, L. and Ponnusamy, A.},
biburl = {https://www.bibsonomy.org/bibtex/283cb4fcc989edb6c8712610bda86cfc2/mozaher},
booktitle = {Database and Expert Systems Applications, 2002. Proceedings. 13th
International Workshop on},
file = {01045883.pdf:Breen2002.pdf:PDF},
interhash = {4b75a6eabf6169027de081ac5b29ecb5},
intrahash = {83cb4fcc989edb6c8712610bda86cfc2},
issn = {1529-4188},
keywords = {Internet, classification, content content-based data, databases, documents, feedback, image information, meta multimedia nets, networks, neural ontologies, precision, query, relevance relevant retrieval, scalable semantic system, user visual},
owner = {Mozaher},
pages = {98--102},
timestamp = {2009-09-12T19:19:37.000+0200},
title = {Image classification using neural networks and ontologies},
year = 2002
}