Learning the Optimal Transformation of Salient Features for Image Classification Zhou, Jun, Fu, Zhouyu and Robles-Kelly, Antonio 2009, Learning the Optimal Transformation of Salient Features for Image Classification, in 2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2009), IEEE,, pp. 125-+, doi: 10.1109/DICTA.2009.28. Attached Files Name Description MIMEType Size Downloads Title Learning the Optimal Transformation of Salient Features for Image Classification Author(s) Zhou, Jun Fu, Zhouyu Robles-Kelly, Antonio orcid.org/0000-0002-2465-5971 Conference name 11th Conference on Digital Image Computing: Techniques and Applications Conference location Melbourne, AUSTRALIA Conference dates 2009/12/01 - 2009/12/03 Title of proceedings 2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2009) Editor(s) Shi, H Zhang, YC Bottema, MJ Lovell, BC Maeder, AJ Publication date 2009 Start page 125 End page + Total pages 3 Publisher IEEE Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Imaging Science & Photographic Technology Computer Science Engineering ATTENTION SCALE ISBN 978-1-4244-5297-2 Language eng DOI 10.1109/DICTA.2009.28 Indigenous content off HERDC Research category E1.1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30125270 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 10 Jul 2019, 14:24:02 EST Thu, 11 Jul 2019, 05:01:17 EST Thu, 11 Jul 2019, 06:03:35 EST Wed, 18 Nov 2020, 10:04:41 EST Wed, 18 Nov 2020, 10:05:47 EST Wed, 18 Nov 2020, 14:06:28 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 23 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 10 Jul 2019, 14:24:02 EST

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