Saliency Detection by Fully Learning A Continuous Conditional Random Field
Artikel i vetenskaplig tidskrift, 2017

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.

Saliency map

Salient object detection

Spatial ranges

Learning

Feature integration

Continuous conditional random field

Författare

Keren Fu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Jie Yang

Shanghai Jiao Tong University

IEEE Transactions on Multimedia

1520-9210 (ISSN)

Vol. 19 1531-1544

Styrkeområden

Transport

Ämneskategorier

Beräkningsmatematik

Systemvetenskap

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.1109/TMM.2017.2679898