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Saliency Detection by Fully Learning A Continuous Conditional Random Field

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang
IEEE transactions on multimedia (1520-9210). Vol. 19 (2017), 7, p. 1531-1544.
[Artikel, refereegranskad vetenskaplig]

Salient object detection is aimed at detecting and segmenting objects that human eyes are most focused on when viewing a scene. Recently, conditional random field (CRF) is drawn renewed interest, and is exploited in this field. However, when utilizing a CRF with unary and pairwise potentials having essential parameters, most existing methods only employ manually designed parameters, or learn parameters partly for the unary potentials. Observing that the saliency estimation is a continuous labeling issue, this paper proposes a novel data driven scheme based on a special CRF framework, the so-called continuous CRF (C-CRF), where parameters for both unary and pairwise potentials are jointly learned. The proposed C-CRF learning provides an optimal way to integrate various unary saliency features with pairwise cues to discover salient objects. To the best of our knowledge, the proposed scheme is the first to completely learn a C-CRF for saliency detection. In addition, we propose a novel formulation of pairwise potentials that enables learning weights for different spatial ranges on a superpixel graph. The proposed C-CRF learning-based saliency model is tested on 6 benchmark datasets and compared with 11 existing methods. Our results and comparisons have provided further support to the proposed method in terms of precision-recall and F-measure. Furthermore, incorporating existing saliency models with pairwise cues through the C-CRF is shown to provide marked boosting performance over individual models.

Nyckelord: Salient object detection, Saliency map, Continuous conditional random field, Learning, Feature integration, Spatial ranges

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Denna post skapades 2017-03-24. Senast ändrad 2017-07-25.
CPL Pubid: 248673


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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)


Tillämpad matematik
Datorseende och robotik (autonoma system)

Chalmers infrastruktur