Saliency detection by combining spatial and spectral information.
Saliency detection by combining spatial and spectral information.
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DOI：
10.1364/ol.38.001987
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发表时间：
2013-06
期刊：
影响因子：
3.6
通讯作者：
Yanbang Zhang;Junwei Han;Lei Guo
中科院分区：
文献类型：
--
作者：
Yanbang Zhang;Junwei Han;Lei Guo
在这封信中，提出了一种新的算法来检测显着区域相结合的空间和光谱信息。首先，在RGB颜色空间和Lab颜色空间中考虑输入图像。其次，在每个通道中同时计算最大对称环绕模型和频谱残差。第三，某些颜色通道中的特征图优于其他通道中的特征图。定义了熵来评价特征图的性能，它可以用来选择合适的通道和联合收割机组合不同的特征图。最后，一个高斯低通滤波器被应用到改善的性能，占中心偏差。实验结果表明，与以往的显著性检测方法相比，本文的显著性检测方法更加有效和鲁棒。
In this Letter, a new algorithm is proposed to detect salient regions by combining spatial and spectral information. First, the input image is considered in both RGB color space and Lab color space. Second, the biggest symmetric surround model and spectral residual are calculated in each channel simultaneously. Third, the feature maps in some color channels outperform the feature maps in the other channels. Entropy is defined to evaluate the performance of the feature maps, which can be used to choose the proper channels and combine different feature maps. Finally, a Gaussian low-pass filter is applied to improve the performance by accounting for the center bias. Compared with previous methods, our saliency detection is more effective and robust as demonstrated by the experiments.