Saliency generation from complex scene via digraph and Bayesian inference
Saliency generation from complex scene via digraph and Bayesian inference
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通过有向图和贝叶斯推理从复杂场景生成显着性
DOI：
10.1016/j.neucom.2015.02.086
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发表时间：
2015-12
期刊：
影响因子：
6
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
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作者：
Wang, Shigang;Yang, Shuyuan;Liu, Zhengkang;Jiao, Licheng
仔细检查计算机视觉的最新进展，我们会发现越来越多的进步可以归因于生物启发算法的引入，这是一个蓬勃发展的计算领域。生物模式可以从神经系统的显著注意原理中产生，它从大背景中弹出最重要的信息。神经机制的新证据表明，生物运动现象发生在更高的认知水平，而不是在感知环境中的低级别像素，特别是在一个复杂的场景。受此启发，本文提出了一种基于有向图模型和多尺度贝叶斯推理的显著性检测方法。我们首先创建一个有向图，超像素作为其节点，并引入一个基线节点，其显着性被认为是零。将基线节点到节点的最短距离定义为节点的显著度，并对Dijkstra算法进行了改进，提高了算法的求解效率。此外，我们将此模型扩展到一个多尺度的版本，以科普不同大小的显着区域和贝叶斯推理策略，通过3D颜色直方图的发展，以实现像素级的显着性。一些基准数据集上的实验结果表明，我们的方法相对于18个国家的最先进的显着性检测方法的优越性，我们的方法达到了最高的召回MSRA-1000。SAR图像舰船检测实验表明，该方法克服了传统恒虚警检测器的不足，在复杂背景下具有更低的虚警率。
Making a close inspection of the recent progress made in computer vision we will find that more and more advancement can be attributed to the introduction of bio-inspired algorithms, which is a flourishing area of computing. Biological patterns can be generated from salient attention principle of the nervous system, which pops out the most important information from large backgrounds. New evidences of the underlying neuronal mechanisms indicate that biological motion phenomena happen on higher cognition level instead of low-level pixels in sensing environment, especially in a complex scene. Inspired by it, this paper proposes a novel saliency detection method based on a directed graph model and multi-scale Bayesian inference. We first create a directed graph with superpixels as its nodes and introduce a baseline node whose saliency is considered to be zero. The saliency of each node is defined as the shortest distance from the baseline node to it and Dijkstra׳s algorithm is adjusted to solve this optimization problem with great efficiency. Furthermore, we extend this model to a multi-scale version to cope with salient regions of different sizes and a Bayesian inference strategy via 3D color histogram is developed to achieve pixel-level saliency. Experimental results on some benchmark dataset demonstrate the superiority of our method with respect to 18 state-of-the-art saliency detection methods and our method achieves the highest recall in MSRA-1000. Additional experiments on ship detection of SAR images show that the proposed method can overcome the shortcomings of traditional CFAR detector and has much fewer false alarms in cluttered background.
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DOI：
10.1109/cvpr.2008.4587715
发表时间：
2008-06
期刊：
2008 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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作者：
Chenlei Guo;Qi Ma;Liming Zhang
通讯作者：
Chenlei Guo;Qi Ma;Liming Zhang
DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
通讯作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
DOI：
10.1109/tpami.2012.89
发表时间：
2013-01-01
影响因子：
23.6
作者：
Borji, Ali;Itti, Laurent
通讯作者：
Itti, Laurent
DOI：
10.1109/tpami.2011.272
发表时间：
2012-10-01
影响因子：
23.6
作者：
Goferman, Stas;Zelnik-Manor, Lihi;Tal, Ayellet
通讯作者：
Tal, Ayellet
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