2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3769-3774
In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.
Object Detection ; Object Segmentation ; Symmetric Matrices ; Image Segmentation ; Quantum Mechanics ; Graph Theory ; Computer Vision ; Graph Affinity Learning ; Salient Object Segmentation ; Spectral Graph Theory
IEEE Conference Publications
View record in IEEE Xplore (Access to full text may be restricted)