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D. Zhu, Y. Luo, L. Dai, X. Shao, Q. Zhou, L. Itti, J. Lu, Salient object detection via a local and global method based on deep residual network, Journal of Visual Communication and Image Representation, Vol. 54, pp. 1-9, Elsevier, Jul 2018. [2017 impact factor: 1.836] (Cited by 9)
Abstract: 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.
Themes: Computational Modeling, Computer Vision
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