Saliency Detection for Stereoscopic Images
Saliency Detection for Stereoscopic Images
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DOI：
10.1109/tip.2014.2305100
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发表时间：
2014-06-01
影响因子：
10.6
通讯作者：
Lin, Weisi
中科院分区：
文献类型：
--
作者：
Fang, Yuming;Wang, Junle;Lin, Weisi
在过去的几十年中，针对各种多媒体处理应用，已经提出了许多用于2D图像的显著性检测模型。目前，立体显示的新兴应用需要新的显著性检测模型来提取显著区域。与二维图像的显著性检测不同，立体图像的显著性检测必须考虑深度特征。在本文中，我们提出了一种新的立体显著性检测框架的基础上的颜色，亮度，纹理和深度的特征对比度。四种类型的特征，即颜色，亮度，纹理和深度，从离散余弦变换系数中提取的特征对比度计算。采用图像块之间的空间距离的高斯模型，以考虑局部和全局对比度计算。然后，设计了一种新的融合方法，将特征图联合收割机，得到最终的立体图像显著图。此外，我们采用中心偏置因子和人类视觉系统的重要特性，以增强立体图像的最终显著图。在眼动跟踪数据库上的实验结果表明，该模型的性能优于其他现有的方法上级。
Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. In addition, we adopt the center bias factor and human visual acuity, the important characteristics of the human visual system, to enhance the final saliency map for stereoscopic images. Experimental results on eye tracking databases show the superior performance of the proposed model over other existing methods.