現在的研究人員都喜歡公布自己文章的代碼，這樣對於別人對自己的文章的理解更一步的加深，也便於別人對自己的算法進行比較和創新。
同時能提高文章的曝光率和引用率。
本文就現有的資源進行鏈接，便於查找和整理。
/****************************************************************
新加坡最近利用頻率進行saliency提取的方法：
https://sites.google.com/site/leofangyuming/
/*************************************************************************
code dataset:
color names:
http://www.csee.wvu.edu/~xinl/source.html
Saliency Map的代碼：
/**********************************************************************************************
1,Yulin Xie, Huchuan Lu,
Visual Saliency Detection Based on Bayesian Model, International Conference on Image
Processing,2011，P645-648，(ICIP2011),(Oral presentation)[PDF][CODE]
來源：http://ice.dlut.edu.cn/lu/publications.html
/*********************************************************************************************************
2,Yulin Xie, Huchuan Lu, Minghsuan Yang,
Bayesian Saliency via Low and Mid Level Cues,
IEEE Transaction On Image Processing,[PDF][CODE] [Project Site]
來源：http://ice.dlut.edu.cn/lu/publications.html
/*********************************************************************************************************
3,Ali Borji, Dicky N. Sihite, and Laurent Itti,
" Salient Object Detection: A Benchmark",
ECCV 2012. [supplement]. Code (~ 19 M). [poster]
來源：http://ilab.usc.edu/~borji/Publications.html
/*********************************************************************************************************
4，Ali Borji and Laurent Itti,
" Exploiting Local and Global Patch Rarities for Saliency Detection,",
IEEE CVPR 2012. [poster] . Code (~ 24 M) . SalMaps [Judd dataset](~ 3.5 M). SalMaps [Bruce&Tsotsos dataset](~ 0.5 M)
來源：http://ilab.usc.edu/~borji/Publications.html
/*********************************************************************************************************
5，Xiaohui Shen and Ying Wu,
"A Unified Approach to Salient Object Detection via Low Rank Matrix Recovery",
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [PDF] [code][project]
來源：http://users.eecs.northwestern.edu/~xsh835/LowRankSaliency.html
/*********************************************************************************************************Federico 6, Perazzi, Philipp Krähenbühl, Yael Pritch and Alexander Hornung
Saliency Filters: Contrast Based Filtering for Salient Region Detection
CVPR 2012 [PDF] [Project Page] [Code]
來源：http://www.stanford.edu/~philkr/
/*********************************************************************************************************
7, Hwann-Tzong Chen
Preattentive Co-Saliency Detection
來源：http://www.cs.nthu.edu.tw/~htchen/
object 相關：
/*********************************************************************************************************
Alexe, B., Deselares, T. and Ferrari, V.
What is an object?
CVPR 2010. [PDF][code][project]
來源：http://groups.inf.ed.ac.uk/calvin/objectness/
segmentation相關：
/*********************************************************************************************************
來源：http://www.cs.toronto.edu/~babalex/research.html
1, Alex Levinshtein, Cristian Sminchisescu, and Sven Dickinson.
Optimal Contour Closure by Superpixel Grouping.
ECCV 2010. Crete, Greece. (Poster, Supplementary material, Code)
2, Alex Levinshtein, Cristian Sminchisescu, and Sven Dickinson.
Multiscale Symmetric Part Detection and Grouping.
ICCV 2009. Kyoto, Japan. (Poster, Supplementary material, Code)
3, Alex Levinshtein, Cristian Sminchisescu, and Sven Dickinson.
ACCV 2010. Queenstown, New Zealand. (Presentation, Website with supplementary material, Spatiotemporal Closure Code, Temporal Turbopixels Code)
4,Alex Levinshtein, Adrian Stere, Kiriakos N. Kutulakos, David J. Fleet, Sven J. Dickinson, and Kaleem Siddiqi.
TurboPixels: Fast Superpixels Using Geometric Flows.
TPAMI 2009 (vol. 31, no. 12). (Supplementary material including code, Just code, including precompiled binaries for Linux and Windows (32 bit) , 64 bit Windows code thanks to Shaul Oren, A modified superpixels.m function that computes the superpixel segmentation image and not just the superpixel boundaries)
/*********************************************************************************************************
【本文會持續更新】