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摘要
Saliency detection is an important task in the computer vision community, especially in visual tracking, image compression, and object recognition tasks. However, the extant saliency detection methods based on RGB or RGB depth (RGB-D) often suffer from problems related to complex backgrounds, illumination, occlusion, and other factors, thereby leading to their inferior detection performance. In this case, a solution for improving the robustness of saliency detection results is warranted. In recent years, commercial and industrial light field cameras based on micro-lens arrays inserted between the main lens and the photosensor have introduced a new method for solving the saliency detection problem. The light field not only records spatial information but also the directions of all incoming light rays. The spatial and angular information inherent in a light field implicitly contains the geometry and reflection characteristics of the observed scene, which can provide reliable prior information for saliency detection, such as background clues and depth information. For example, the digital refocus technique can divide the light field into focal slices that focus at different depths. The background clues can be obtained from the focused areas. The light field contains effective saliency object occlusion information. Depth information also be obtained from the light field in various ways. Therefore, using light fields offers many advantages in dealing with problems related to saliency detection. Although saliency detection based on light fields has received much attention in recent years, a deep understanding of this method is yet to be achieved. In this paper, we review the research progress on light field saliency detection to build a foundation for future studies on this topic. First, we briefly discuss light field imaging theory, light field cameras, and the existing light field datasets used for saliency detection and then point out the differences among various datasets. Second, we systematically review the extant algorithms and the latest progress in light filed saliency detection from the aspects of hand-crafted features, sparse coding, and deep learning. Saliency detection algorithms based on light field hand-crafted features are generally based on the idea of contrast. These algorithms detect salient regions by calculating the feature difference between each pixel and super pixel as well as between other pixels and other super pixels. Saliency detection based on sparse coding and deep learning follow the same idea of feature learning, that is, they use image feature coding or the outstanding feature representation abilities of convolution network to determine the salient regions. By analyzing the experimental data on four publicly available light field saliency detection datasets, we summarize the advantages and disadvantages of the existing light field saliency detection methods, summarize the recent progress in light-field-based saliency detection and point out the limitations in this field. Only a few light field datasets are presently available for saliency detection, and these datasets are all generated by light field cameras based on micro-lens array, which has a narrow baseline. Therefore, the effective utilization of various information present in a light field remains a challenge. While saliency detection algorithms based on light fields have been proposed in previous studies, saliency detection based on light fields warrant further study due to the complexity of real scenes. ? 2020, Editorial and Publishing Board of Journal of Image and Graphics. All right reserved.
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