Abstract
Visual saliency detection inspired by brain cognitive mechanisms is an important component of computer vision, aiming at automatically highlight salient visual objects from the image background. In complex real scenarios, integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting saliency detection performance. In this paper, we propose a novel collaborative algorithm for saliency detection by adaptively incorporating information from grayscale and thermal images. Specifically, we first construct an affinity graph model via robust graph learning to characterize the feature similarities and location relationships between two different image patches under each modal. Based on these affinity graphs, a collaborative manifold ranking method cross-modality consistent constraints is designed to effectively and efficiently infer the salient score for each patch. Moreover, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking scheme to achieve an adaptive weighted fusion of different source data. For optimization, we propose some iterative algorithms to efficiently solve the models with several subproblems. Experiments on the grayscale-thermal datasets demonstrate the superior performance of the new method over the state-of-the-art approaches.
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Acknowledgements
This work was supported by the Key Natural Science Project of Anhui Provincial Education Department (KJ2018A0023), the Guangdong Province Science and Technology Plan Projects (2017B010110011), the Anhui Key Research and Development Plan (1804a09020101), the National Basic Research Program (973 Program) of China (2015CB351705) and the National Natural Science Foundation of China (61906002, 61402002, 61876002 and 61860206004).
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Sun, D., Li, S., Ding, Z., Luo, B. (2020). RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_57
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DOI: https://doi.org/10.1007/978-981-15-3415-7_57
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