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		<title>Aesthetic image rating (AIR) algorithm - Versionsgeschichte</title>
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		<title>Gubachelier: Die Seite wurde neu angelegt: „ == Reference == Reaves, David: Aesthetic image rating (AIR) algorithm. Ph.D. thesis University of Texas at Austin (2008)    == DOI ==  == Abstract == Rapi…“</title>
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		<summary type="html">&lt;p&gt;Die Seite wurde neu angelegt: „ == Reference == Reaves, David: &lt;a href=&quot;/index.php?title=Aesthetic_image_rating_(AIR)_algorithm&quot; title=&quot;Aesthetic image rating (AIR) algorithm&quot;&gt;Aesthetic image rating (AIR) algorithm&lt;/a&gt;. Ph.D. thesis University of Texas at Austin (2008)    == DOI ==  == Abstract == Rapi…“&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Neue Seite&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Reference ==&lt;br /&gt;
Reaves, David: [[Aesthetic image rating (AIR) algorithm]]. Ph.D. thesis University of Texas at Austin (2008)  &lt;br /&gt;
&lt;br /&gt;
== DOI ==&lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
Rapidly advancing technologies offer a greater volume of people the possi-&lt;br /&gt;
bility to both create and consume information.  And, with this widening of&lt;br /&gt;
opportunity, the volume of digital information has increased in mammoth&lt;br /&gt;
proportion.  Indeed, this age of information is marked by quantity, but what&lt;br /&gt;
of quality?  It has become necessary to formulate a systematic method to&lt;br /&gt;
sift  through  the  vast  amount  of  data.   This  paper  presents  an  algorithm&lt;br /&gt;
that seeks to emulate the manner by which a human might judge an image&amp;#039;s&lt;br /&gt;
aesthetic value.  The notion that a machine could imitate human thought&lt;br /&gt;
processes is not necessarily novel, and, as such, a fair amount of work has&lt;br /&gt;
been done regarding algorithmic aesthetic digital image rating. Most of these&lt;br /&gt;
proposed algorithms, however, have been unable to satisfactorily mimic ac-&lt;br /&gt;
tual  human  ratings.   This  paper  builds  on  these  past  works  and  yet  goes&lt;br /&gt;
further by significantly improving on these prior accomplishments.  The re-&lt;br /&gt;
sult of our focus on the discovery of an optimal vector of image features is&lt;br /&gt;
a highly accurate emulation of human ratings&lt;br /&gt;
&lt;br /&gt;
== Extended Abstract ==&lt;br /&gt;
&lt;br /&gt;
== Bibtex == &lt;br /&gt;
&lt;br /&gt;
== Used References ==&lt;br /&gt;
[1] The iLab Neuromorphic Vision C++ Toolkit.&lt;br /&gt;
&lt;br /&gt;
[2]  Rudolf Arnheim. Art and Visual Perception:  A Psychology of the Cre-ative Eye.  University of California Press, 1974.&lt;br /&gt;
&lt;br /&gt;
[3]  Chih-Chung   Chang   and   Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. &lt;br /&gt;
Software    available    at http://www.csie.ntu.edu.tw/cjlin/libsvm.&lt;br /&gt;
&lt;br /&gt;
[4]  Yi-Wei Chen and Chih-Jen Lin. Combining SVMs with Various Feature Selection Strategies, 2005.&lt;br /&gt;
&lt;br /&gt;
[5]  Corbis. Corbis Corporate Fact Sheet.&lt;br /&gt;
&lt;br /&gt;
[6]  R. Datta, D. Joshi, J. Li, and J.Z. Wang.  Studying aesthetics in photographic images using a computational approach.  pages III: 288-301, 2006.&lt;br /&gt;
&lt;br /&gt;
[7]  Ritendra Datta, Jia Li, and James Z. Wang.  Learning the consensus on visual quality for next-generation image management.  In MULTI-MEDIA &amp;#039;07:  Proceedings of the 15th international conference on Multimedia, pages 533{536, New York, NY, USA, 2007. ACM.&lt;br /&gt;
&lt;br /&gt;
[8]  P. Golland.  Discriminative direction for kernel classifiers, 2001.&lt;br /&gt;
&lt;br /&gt;
[9]  The Guardian. Together in electric dreams, January 2005.&lt;br /&gt;
&lt;br /&gt;
[10]  L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE  Transactions  on  Pattern  Analysis and Machine Intelligence, 20(11):1254-1259, Nov 1998.&lt;br /&gt;
&lt;br /&gt;
[11]  Yan Ke, Xiaoou Tang, and Feng Jing. The design of high-level features for photo quality assessment.  In Computer Vision and Pattern Recognition,  2006  IEEE  Computer  Society  Conference  on, volume 1, pages 419-426, 2006.&lt;br /&gt;
&lt;br /&gt;
[12]  Anthony  Santella,  Maneesh  Agrawala,  Doug  DeCarlo,  David  Salesin, and Michael Cohen.  Gaze-based interaction for semi-automatic photo cropping. In CHI &amp;#039;06:  Proceedings of the SIGCHI conference on Human Factors  in  computing  systems,  pages  771{780,  New  York,  NY,  USA, 2006. ACM.&lt;br /&gt;
&lt;br /&gt;
[13]  Hanghang  Tong,  Mingjing  Li,  Hong-Jiang  Zhan,  Jingrui  He,  and Changshui Zhang.  Classification of digital photos taken by photographers or home users. In Advances in Multimedia Information Processing - PCM 2004, volume 3331/2005. Springer Berlin / Heidelberg, 2004.&lt;br /&gt;
&lt;br /&gt;
[14]  Hanghang Tong, Mingjing Li, Hongjiang Zhang, and Changshui Zhang. Blur detection for digital images using wavelet transform. Multimedia and  Expo,  2004.  ICME  &amp;#039;04.  2004  IEEE  International  Conference  on, 1:17{20 Vol.1, 27-30 June 2004.&lt;br /&gt;
&lt;br /&gt;
[15]  David Tschumperl�e. The  CImg  Library  :  C++  Template  Image  Processing Library.&lt;br /&gt;
&lt;br /&gt;
[16]  G.   Uytterhoeven,   F.   Van   Wulpen,   M.   Jansen,   D.   Roose,   and A.  Bultheel.   WAILI:  A  software  library  for  image  processing  using integer wavelet transforms.  In K.M. Hanson, editor, Medical  Imaging 1998:  Image  Processing, volume 3338, pages 1490{1501. InternationalSociety for Optical Engineering, 1998.&lt;br /&gt;
&lt;br /&gt;
[17]  T. W. Whitfield and T. J. Wiltshire. Color psychology: a critical review. Genetic, Social, and General Psycology Monographs, 1990&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Links ==&lt;br /&gt;
=== Full Text === &lt;br /&gt;
http://www.cs.utexas.edu/ftp/techreports/honor_theses/cs-08-04-reaves.pdf&lt;br /&gt;
&lt;br /&gt;
[[intern file]]&lt;br /&gt;
&lt;br /&gt;
=== Sonstige Links ===&lt;/div&gt;</summary>
		<author><name>Gubachelier</name></author>	</entry>

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