我的第一篇paper
找实习虽然只为自己争取到一个秋季的绿色通道，但可喜的是，我投的几篇paper，终于中了一篇。
现在可以在英文数据库或google scholar上面搜索到自己名字，感觉很nice，研究生的心愿算是完成了一部分，至于剩下的中不中，都不那么重要了，已经留下了自己在科研道路上的足迹。
投递的杂志是Signal Processing，是一个很不错的杂志，从ACCEPT到文章上线速度很快，在我校的评级是B类期刊，发表一篇达到学校博士毕业的基本要求（一篇B或者2篇C），审稿周期算是中等吧，这篇文章的周期大约是7个月。
Automatic image segmentation using salient key point extraction and star shape prior
Xiangli Liao, Hongbo Xu, Yicong Zhou, Kunqian Li, Wenbing Tao, Qiuju Guo, Liman Liu
ARTICLE INFO
Article history: Received 27 September 2013 Received in revised form 28 March 2014 Accepted 29 April 2014
Please cite this article as: X. Liao, et al., Automatic image segmentation using salient key point extraction and star
shape prior, Signal Processing (2014)
ABSTRACT
In this paper, a new unsupervised segmentation method is proposed. The method integrates the star shape prior of the image object with salient point detection algorithm. In the proposed method, the Harris salient point detection is first applied to the color image to obtain the initial salient points. A regional contrast based saliency extraction method is then used to select rough object regions in the image. To restrict the distribution of salient points, an adaptive threshold segmentation is applied to the saliency map to get the saliency mask. And then the salient region points can be obtained by placing the saliency mask on the initial Harris salient points. In order to make sure the salient points which we get are inside the image object thus the star shape constraint can be applied to the graph cuts segmentation, the Affinity Propagation (AP) clustering is employed to find the salient key points among the salient region points. Finally, these salient key points are regarded as foreground seeds and the star shape prior is introduced to graph cuts segmentation framework to extract the foreground object. Extensive experiments and comparisons on public database are provided to demonstrate the good performance of the proposed method.&2014 Published by Elsevier B.V.
