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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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<TITLE>CAIP2017</TITLE>
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<a href="https://github.com/ChunbiaoZhu/CAIP2017" class="github-corner" aria-label="View source on Github"><svg width="80" height="80" viewBox="0 0 250 250" style="fill:#fff; color:#151513; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="M128.3,109.0 C113.8,99.7 119.0,89.6 119.0,89.6 C122.0,82.7 120.5,78.6 120.5,78.6 C119.2,72.0 123.4,76.3 123.4,76.3 C127.3,80.9 125.5,87.3 125.5,87.3 C122.9,97.6 130.6,101.9 134.4,103.2" fill="currentColor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
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<TD colspan="3"><DIV align="center"><STRONG>CAIP2017 - 17th international Conference on Computer Analysis of Images and Patterns</STRONG></DIV></TD>
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<TD colspan="3"><DIV align="center" class="xlarge">A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining</DIV></TD>
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<div class="authorname" style="font-size:large;line-adjust:0">Chunbiao Zhu<sup>1</sup>, Ge Li<sup>1*</sup>, Xiaoqiang Guo<sup>2</sup>, Ronggang Wang<sup>1</sup>, Wenmin Wang<sup>1</sup></div>
<div class="authoraddress" style="line-adjust:0"><sup>1</sup>School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China <br /> <sup>2</sup>Academy of Broadcasting Science, SAPPRFT Beijing, China</div>
<div class="authoremail email" style="line-adjust:0">*geli@ece.pku.edu.cn</div>
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<a href="https://github.com/ChunbiaoZhu/CAIP2017">Source Code Available</a>
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<figcaption>Fig.1 Our Framework.</figcaption>
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<figcaption>Fig.2 Visual Process of Our Framework.</figcaption>
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<TD width="400"><div align="justify">Saliency detection is an active topic in multimedia field. Several algorithms have been proposed in this field. Most previous works on saliency detection focus on 2D images. However, for some complex situations which contain multiple objects or complex background, they are not robust and their performances are not satisfied. Recently, 3D visual information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The evaluation of the proposed algorithm on two challenging datasets shows that our algorithm outperforms state-of-the-art.
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<PRE>@inproceedings{Kwon:2015:TPAMI<BR> author = {Younghee Kwon and Kwang In Kim and James Tompkin and Jin Hyung Kim and Christian Theobalt},<BR> title = {Efficient Learning of Image Super-resolution and Compression Artifact Removal<BR> with Semi-local {Gaussian} Processes},<BR> booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},<BR> volume = {37},<BR> number = {9},<BR> pages = {1792--1805},<BR> year = {2015}<BR>}</PRE>
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<TD align="center" valign="middle"><A href="./EfficientLearning-basedImageEnhancement_TPAMI2014_Paper.pdf" target="_self"><IMG src="./paper_thumbnail.png" BORDER=0 ></A></TD>
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<TD> <A href="./EfficientLearning-basedImageEnhancement_TPAMI2014_Supplemental.pdf" target="_self"><IMG src="./supplemental_thumbnail.png" BORDER=0 ></A></TD>
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Paper<BR>
<A href="./EfficientLearning-basedImageEnhancement_TPAMI2014_Paper.pdf" target="_self">PDF (3 MB)</A>
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Supplemental Material<BR>
<A href="./EfficientLearning-basedImageEnhancement_TPAMI2014_Supplemental.pdf" target="_self">PDF (32 MB)</A>
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<TD class="large" align = "center">Experimental Results </TD>
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<figcaption align = "center">Fig.3 Left:PR curve of different methods on RGBD1* dataset. Right:PR curve of different methods on RGBD2* dataset.</figcaption>
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<figcaption align = "center">Fig.4 Left:ROC curve of different methods on RGBD1* dataset. Right:ROC curve of different methods on RGBD2* dataset.</figcaption>
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<figcaption align = "center">Fig.5 Visual comparison of saliency maps on two datasets.
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<TD class="large">Acknowledgements</TD>
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<TD>This work was supported by the grant of National Natural Science Foundation of China (No.U1611461), the grant of Science and Technology Planning Project of Guangdong Province, China (No.2014B090910001), the grant of Guangdong Province Projects of 2014B010117007 and the grant of Shenzhen Peacock Plan (No.20130408-183003656).</TD>
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<TD>Images courtesy of <a href="http://www.itl.nist.gov/iad/humanid/feret/feret_master.html">The Facial Recognition Technology (FERET) Database</a>,
<a href="http://r0k.us/graphics/kodak/">Kodak Lossless True Color Image Suite</a>,
<a href="http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">The Berkeley Segmentation Dataset</a>, <a href="http://en.wikipedia.org/wiki/Lenna">Lena</a> <a href="http://www.ece.rice.edu/~wakin/images/">courtesy</a> of <a href="http://www.computableminds.com/post/lena-soderberg-common-image-processing-test-images.html">Playboy</a>, and <a href="http://nuit-blanche.blogspot.com/2012/03/let-there-be-only-one-fabio.html?m=1">Fabio</a> courtesy of <a href="http://en.wikipedia.org/wiki/Fabio_Lanzoni">Fabio Lanzoni</a> (agent: Eric Ashenberg) via <a href="http://www.cmc.edu/pages/faculty/DNeedell/index.php">Deanna Needell</a> - thanks!</TD>
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