Visual saliency detection model simulates the human visual system to perceive
the scene, and has been widely used in many vision tasks. With the acquisition
technology development, more comprehensive information, such as depth cue,
inter-image correspondence, or temporal relationship, is available to extend
image saliency detection to RGBD saliency detection, co-saliency detection, or
video saliency detection. RGBD saliency detection model focuses on extracting
the salient regions from RGBD images by combining the depth information.
Co-saliency detection model introduces the inter-image correspondence
constraint to discover the common salient object in an image group. The goal of
video saliency detection model is to locate the motion-related salient object
in video sequences, which considers the motion cue and spatiotemporal
constraint jointly. In this paper, we review different types of saliency
detection algorithms, summarize the important issues of the existing methods,
and discuss the existent problems and future works. Moreover, the evaluation
datasets and quantitative measurements are briefly introduced, and the
experimental analysis and discission are conducted to provide a holistic
overview of different saliency detection methods.

本论文综述不同类型的显着性检测算法，并总结现有方法的重要问题，分析了存在的问题和未来的工作。此外，简要介绍了评估数据集和定量测量，并进行了实验分析和讨论，以提供不同显著性检测方法的整体概述。

具有全面信息的视觉显著性检测综述

Review of Visual Saliency Detection with Comprehensive Information

Numerous efforts have been made to design different low level saliency cues
for the RGBD saliency detection, such as color or depth contrast features,
background and color compactness priors. However, how these saliency cues
interact with each other and how to incorporate these low level saliency cues
effectively to generate a master saliency map remain a challenging problem. In
this paper, we design a new convolutional neural network (CNN) to fuse
different low level saliency cues into hierarchical features for automatically
detecting salient objects in RGBD images. In contrast to the existing works
that directly feed raw image pixels to the CNN, the proposed method takes
advantage of the knowledge in traditional saliency detection by adopting
various meaningful and well-designed saliency feature vectors as input. This
can guide the training of CNN towards detecting salient object more effectively
due to the reduced learning ambiguity. We then integrate a Laplacian
propagation framework with the learned CNN to extract a spatially consistent
saliency map by exploiting the intrinsic structure of the input image.
Extensive quantitative and qualitative experimental evaluations on three
datasets demonstrate that the proposed method consistently outperforms
state-of-the-art methods.

本文提出了一种新的卷积神经网络来融合不同的 RGBD 低级显著性线索，利用 Laplacian 传播框架提取空间一致的显著性图，并在三个数据集上进行了广泛的实验评估，结果表明这种方法始终优于现有的最先进方法。