Abstract
In the state-of-the-art saliency detection methods based on contrast priors, little attention is paid on the region smoothness constraints. The paper proposes a two-stage saliency detection method in which a smoothness prior is explicitly involved in a continuous Conditional Random Field (CRF). In stage one, we construct a continuous CRF based on the sparse codes of perceptual features on all locations, and minimize the energy of CRF to obtain discrimination maps. In stage two, we train a discriminative machine and learn the saliency maps from discrimination maps, aiming to take the human attention priors into consideration. Our experiments on MSRA-1000 show that the new method is effective against the state-of-the-art methods.
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Zhao, Q., Li, W., Wang, F., Yin, B. (2014). Two-Stage Saliency Detection Based on Continuous CRF and Sparse Coding. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_47
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DOI: https://doi.org/10.1007/978-3-662-45646-0_47
Publisher Name: Springer, Berlin, Heidelberg
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