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Visual saliency by extended quantum cuts | IEEE Conference Publication | IEEE Xplore

Visual saliency by extended quantum cuts


Abstract:

In this study, we propose an unsupervised, state-of-the-art saliency map generation algorithm which is based on a recently proposed link between quantum mechanics and spe...Show More

Abstract:

In this study, we propose an unsupervised, state-of-the-art saliency map generation algorithm which is based on a recently proposed link between quantum mechanics and spectral graph clustering, Quantum Cuts. The proposed algorithm forms a graph among superpixels extracted from an image and optimizes a criterion related to the image boundary, local contrast and area information. Furthermore, the effects of the graph connectivity, superpixel shape irregularity, superpixel size and how to determine the affinity between superpixels are analyzed in detail. Furthermore, we introduce a novel approach to propose several saliency maps. Resulting saliency maps consistently achieves a state-of-the-art performance in a large number of publicly available benchmark datasets in this domain, containing around 18k images in total.
Date of Conference: 27-30 September 2015
Date Added to IEEE Xplore: 10 December 2015
ISBN Information:
Conference Location: Quebec City, QC, Canada

References

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