TY - GEN
T1 - Sports image classification through Bayesian classifier
AU - Jung, Youna
AU - Hwang, Eenjun
AU - Kim, Wonil
PY - 2004
Y1 - 2004
N2 - Most documents on the web today contain image as well as text data. So far, classification of images has been dependent on the annotation. For the more efficient and accurate classification, not only textual data but also image contents should be considered. In this paper, we propose a novel method for classifying specific image data; sports images. The proposed method is based on the Bayesian framework and employs four important color features to exploit the properties of sports images.
AB - Most documents on the web today contain image as well as text data. So far, classification of images has been dependent on the annotation. For the more efficient and accurate classification, not only textual data but also image contents should be considered. In this paper, we propose a novel method for classifying specific image data; sports images. The proposed method is based on the Bayesian framework and employs four important color features to exploit the properties of sports images.
UR - http://www.scopus.com/inward/record.url?scp=7444256203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=7444256203&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-25945-9_54
DO - 10.1007/978-3-540-25945-9_54
M3 - Conference contribution
AN - SCOPUS:7444256203
SN - 3540222189
SN - 9783540222187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 546
EP - 555
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Conejo, Ricardo
A2 - Perez-de-la-Cruz, Jose-Luis
A2 - Urretavizcaya, Maite
PB - Springer Verlag
T2 - 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003
Y2 - 12 November 2003 through 14 November 2003
ER -