A Two-Stage Bayesian Integration Framework for Salient Object Detection on Light Field

Wang, Anzhi; Wang, Minghui; Li, Xiaoyan; Mi, Zetian; Zhou, Huan

doi:10.1007/s11063-017-9610-x

Cited by 30 publications

(13 citation statements)

References 27 publications

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“…BIF [52] used a Bayesian framework to fuse multiple features extracted from RGB images, depth MA [53] measured the saliency of a superpixel by computing the intra-cue distinctiveness between pairs of superpixels, where features considered included color, depth, and flow inherited from different focal planes and multiple viewpoints. The light-field flow was first employed in this method, estimated from focal stacks and multi-view sequences, to capture depth discontinuities/contrast.…”

Section: Traditional Modelsmentioning

confidence: 99%

“…Thus, in this paper, we conduct the first comprehensive review and benchmark for light field SOD. We review previous studies on light field SOD, including ten traditional models [1,5,30,[50][51][52][53][54][55][56], seven deep learning-based models [31,32,45,[57][58][59][60], one comparative study [48], and one brief review [49]. In addition, we also review existing light field SOD datasets [5,45,53,57,59], and statistically analyze them, covering object size, distance between object and image center, number of focal slices, and number of objects.…”

Section: Introductionmentioning

confidence: 99%

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Light field salient object detection: A review and benchmark

Jiang

Zhou

et al. 2022

Comp. Visual Media

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Salient object detection (SOD) is a long-standing research topic in computer vision with increasing interest in the past decade. Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways, using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend. This paper provides the first comprehensive review and a benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, a comparative study, and a brief review. Existing datasets for light field SOD are also summarized. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, providing insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models. Due to the inconsistency of current datasets, we further generate complete data and supplement focal stacks, depth maps, and multi-view images for them, making them consistent and uniform. Our supplemental data make a universal benchmark possible. Lastly, light field SOD is a specialised problem, because of its diverse data representations and high dependency on acquisition hardware, so it differs greatly from other saliency detection tasks. We provide nine observations on challenges and future directions, and outline several open issues. All the materials including models, datasets, benchmarking results, and supplemented light field datasets are publicly available at https://github.com/kerenfu/LFSOD-Survey.

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Section: Traditional Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Light field salient object detection: A review and benchmark

Jiang

Zhou

et al. 2022

Comp. Visual Media

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“…The focus information of the background is suppressed [ 30 ]. Later, he (2017) proposed saliency maps generated by a probabilistic fusion of different cues using a Bayesian framework [ 31 ]. To a certain extent, this makes up for the inaccurate detection in different scenarios caused by the fixed weight value when the previous algorithm adopts the linear weighted fusion method.…”

Section: Related Workmentioning

confidence: 99%

Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut

Duan

Guan

et al. 2022

Sensors

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In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.

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“…For example, Zhang et al [131] utilized a set of focal slices to compute the background prior, and then incorporate it with the location prior for SOD. Wang et al [134] proposed a two-stage Bayesian fusion model to integrate multiple contrasts for boosting SOD performance. Recently, several deep learning-based light field SOD models [139]- [142], [144], [145] have been developed and obtained remarkable performance.…”

Section: Saliency Detection On Light Field a Light Field Sod Modelsmentioning

confidence: 99%

RGB-D Salient Object Detection: A Survey

Zhou,

Fan,

Cheng

et al. 2020

Preprint

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A Two-Stage Bayesian Integration Framework for Salient Object Detection on Light Field

Cited by 30 publications

References 27 publications

Light field salient object detection: A review and benchmark

Light field salient object detection: A review and benchmark

Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut

RGB-D Salient Object Detection: A Survey

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