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A key problem in co-saliency detection is how to effectively model the interactive relationship of a whole image group and the individual perspective of each image in a united data-driven manner. In this paper, we propose a group-wise deep co-saliency detection approach to address the co-saliency object discovery problem based on the fully convolutional network (FCN). The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single image individual feature representation, and model this in a collaborative learning framework. Then, we set up a unified deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
This article was published in the following journal.
Name: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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