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首页> 外文期刊>Journal of visual communication & image representation >Salient object detection via a local and global method based on deep residual network
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Salient object detection via a local and global method based on deep residual network

机译：基于深度残差网络的局部和全局方法显着目标检测

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摘要

Salient object detection is a fundamental problem in both pattern recognition and image processing tasks. Previous salient object detection algorithms usually involve various features based on priors/assumptions about the properties of the objects. Inspired by the effectiveness of recently developed deep feature learning, we propose a novel Salient Object Detection via a Local and Global method based on Deep Residual Network model (SOD-LGDRN) for saliency computation. In particular, we train a deep residual network (ResNet-G) to measure the prominence of the salient object globally and extract multiple level local features via another deep residual network (ResNet-L) to capture the local property of the salient object. The final saliency map is obtained by combining the local-level and global-level saliency via Bayesian fusion. Quantitative and qualitative experiments on six benchmark datasets demonstrate that our SOD-LGDRN method outperforms eight state-of-the-art methods in the salient object detection.
机译：显着物体检测是模式识别和图像处理任务中的基本问题。先前的显着物体检测算法通常基于关于物体特性的先验/假设涉及各种特征。受最近开发的深度特征学习的有效性启发，我们提出了一种基于深度和残差网络模型（SOD-LGDRN）的局部和全局方法进行显着性计算的显着目标检测。特别是，我们训练一个深度残差网络（ResNet-G）来全局测量突出对象的突出程度，并通过另一个深度残差网络（ResNet-L）提取多级局部特征以捕获突出对象的局部属性。最终的显着性图是通过贝叶斯融合将局部和全局的显着性相结合而获得的。在六个基准数据集上进行的定性和定量实验表明，在显着目标检测中，我们的SOD-LGDRN方法优于八种最新方法。

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