Saliency ranking using deep learning

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Kalash, Mahmoud
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Salient object detection is a problem that has been considered in detail and many solutions proposed. In this thesis, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried which implies a relative rank exists on salient objects. In this thesis, we solve this more general problem that considers relative rank. A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise refinement to address both of the saliency ranking and subitizing tasks. We also present methods for deriving suitable ranked salient object instances to generate a large scale dataset for saliency ranking, along with metrics suitable to measuring success in a relative object saliency landscape. Our approach exceeds performance of any prior work across all metrics considered (both traditional and newly proposed).
Deep Learning, Convolutional Neural Networks, Saliency Detection, Saliency Ranking
M. A. Islam*, M. Kalash* and N. Bruce. "Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects." 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA. IEEE, June 2018.