IEEE Trans Image Process. 2014 Jun;23(6):2625-36. doi: 10.1109/TIP.2014.2305100.
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.
在过去几十年中，已经提出了许多用于各种多媒体处理应用的 2D 图像显著检测模型。目前，立体显示的新兴应用需要新的显著检测模型来提取显著区域。与 2D 图像的显著检测不同，立体图像的显著检测必须考虑深度特征。在本文中，我们提出了一种基于颜色、亮度、纹理和深度特征对比度的新的立体显著检测框架。从离散余弦变换系数中提取四种类型的特征，即颜色、亮度、纹理和深度，用于特征对比度计算。采用图像块之间的空间距离高斯模型来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来组合特征图，以获得立体图像的最终显著图。此外，我们采用了中心偏差因子和人类视觉敏锐度，这是人类视觉系统的重要特征，来增强立体图像的最终显著图。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方法。
