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\title{Global Contrast based Salient Region Detection 
    \footnote{Project page:
    \href{http://cg.cs.tsinghua.edu.cn/people/\~cmm/saliency/}
    {http://cg.cs.tsinghua.edu.cn/people/$\sim$cmm/saliency/}} 
}

\author{\small Ming-Ming Cheng$^{1}$\quad Guo-Xin Zhang$^{1}$ \quad Niloy J. Mitra$^{2}$
    \quad Xiaolei Huang$^{3}$  \quad Shi-Min Hu$^{1}$  \\
    $^{1}$ TNList, Tsinghua University \quad \quad
    $^2$ KAUST   \quad \quad $^3$ Lehigh University\\
    {\tt \small chengmingvictor@gmail.com}
}

\date{}
\maketitle

\begin{abstract}
Reliable estimation of visual saliency allows appropriate processing of images without prior
knowledge of their contents, and thus remains an important step in many computer vision tasks
including image segmentation, object recognition, and adaptive compression.
We propose a regional contrast based saliency extraction algorithm,
which simultaneously evaluates global contrast differences and spatial coherence.
The proposed algorithm is simple, efficient, and yields full resolution saliency maps.
Our algorithm consistently outperformed existing saliency detection methods, yielding
higher precision and better recall rates, when evaluated using one of the largest
publicly available data sets.
We also demonstrate how the extracted saliency map can be used to create high quality
segmentation masks for subsequent image processing.
\end{abstract}


\textbf{Keywords:}\quad
Saliency detection, global contrast, early and biologically-inspired Vision.



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{\footnotesize % "\tiny", "\scriptsize", "\footnotesize", "\small", "\normalsize", "\large", "\Large", "\LARGE", "\huge", "\Huge"
\begin{verbatim}
@conference{11cvpr/Cheng_Saliency,
  title={Global Contrast based Salient Region Detection},
  author={Ming-Ming Cheng and Guo-Xin Zhang and Niloy J. Mitra and Xiaolei Huang and Shi-Min Hu},
  booktitle={IEEE CVPR},
  pages={409--416},
  year={2011},
}
\end{verbatim}
}

\end{document}