Learning full-range affinity for diffusion-based saliency detection

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range aff...

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Bibliographic Details
Main Authors: Keren Fu, Gu, Irene Y. H., Jie Yang
Format: Conference Proceeding
Language:English
Subjects:
Affinity
affinity learning
Benchmark testing
Benchmarking
Computational modeling
Conferences
Detectors
Diffusion
Electronics
graph-based diffusion
Graphs
Image color analysis
Image edge detection
Learning
Manifolds
Saliency detection
semi-supervised learning
Speech
Symmetric matrices
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Summary:In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2016.7472012