scholarly journals Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 838
Author(s):  
Jiajia Wu ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Hang Yang ◽  
Huiyuan Luo ◽  
...  
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Color Space ◽  
Saliency Map ◽  
Depth Information ◽  
Detection Model ◽  
Absorbing Markov Chain ◽  
Detection Failure ◽  
Benchmark Datasets ◽  
High Level

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

Hierarchical Saliency Detection via Probabilistic Object Boundaries

2017 ◽  
Vol 31 (06) ◽  
pp. 1755010 ◽  
Author(s):  
Haijun Lei ◽  
Hai Xie ◽  
Wenbin Zou ◽  
Xiaoli Sun ◽  
Kidiyo Kpalma ◽  
...  
Keyword(s):  
Computational Models ◽  
Saliency Detection ◽  
Spatial Coherence ◽  
Saliency Map ◽  
Object Boundary ◽  
Detection Model ◽  
Saliency Maps ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Center Bias

Though there are many computational models proposed for saliency detection, few of them take object boundary information into account. This paper presents a hierarchical saliency detection model incorporating probabilistic object boundaries, which is based on the observation that salient objects are generally surrounded by explicit boundaries and show contrast with their surroundings. We perform adaptive thresholding operation on ultrametric contour map, which leads to hierarchical image segmentations, and compute the saliency map for each layer based on the proposed robust center bias, border bias, color dissimilarity and spatial coherence measures. After a linear weighted combination of multi-layer saliency maps, and Bayesian enhancement procedure, the final saliency map is obtained. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed model outperforms eight state-of-the-art saliency detection models.

An Efficient Saliency Detection Model Based on Wavelet Generalized Lifting

2018 ◽  
Vol 33 (02) ◽  
pp. 1954006 ◽  
Author(s):  
Xin Zhong ◽  
Frank Y. Shih
Keyword(s):  
Saliency Detection ◽  
Saliency Map ◽  
Human Vision ◽  
Wavelet Coefficients ◽  
Detection Model ◽  
Model Based ◽  
Full Resolution ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Object Mask

Saliency detection refers to the segmentation of all visually conspicuous objects from various backgrounds. The purpose is to produce an object-mask that overlaps the salient regions annotated by human vision. In this paper, we propose an efficient bottom-up saliency detection model based on wavelet generalized lifting. It requires no kernels with implicit assumptions and prior knowledge. Multiscale wavelet analysis is performed on broadly tuned color feature channels to include a wide range of spatial-frequency information. A nonlinear wavelet filter bank is designed to emphasize the wavelet coefficients, and then a saliency map is obtained through linear combination of the enhanced wavelet coefficients. This full-resolution saliency map uniformly highlights multiple salient objects of different sizes and shapes. An object-mask is constructed by the adaptive thresholding scheme on the saliency maps. Experimental results show that the proposed model outperforms the existing state-of-the-art competitors on two benchmark datasets.

scholarly journals Saliency Detection by Multilevel Deep Pyramid Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Wang ◽  
Lei Dai ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Yong Zhang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Saliency Detection ◽  
Saliency Map ◽  
Multiple Features ◽  
Low Level ◽  
Pyramid Model ◽  
High Level ◽  
Different Levels ◽  
Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

Prior Fusion and Feature Transformation-Based Principal Component Analysis for Saliency Detection

2018 ◽  
Vol 32 (08) ◽  
pp. 1855013
Author(s):  
Dongjing Shan ◽  
Chao Zhang
Keyword(s):  
Principal Component Analysis ◽  
Saliency Detection ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Saliency Map ◽  
Feature Transformation ◽  
Benchmark Datasets ◽  
Pca Method ◽  
Linear Transform

In this paper, we propose a prior fusion and feature transformation-based principal component analysis (PCA) method for saliency detection. It relies on the inner statistics of the patches in the image for identifying unique patterns, and all the processes are done only once. First, three low-level priors are incorporated and act as guidance cues in the model; second, to ensure the validity of PCA distinctness model, a linear transform for the feature space is designed and needs to be trained; furthermore, an extended optimization framework is utilized to generate a smoothed saliency map based on the consistency of the adjacent patches. We compare three versions of our model with seven previous methods and test them on several benchmark datasets. Different kinds of strategies are adopted to evaluate the performance and the results demonstrate that our model achieves the state-of-the-art performance.

Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability

2018 ◽  
Vol 27 (2) ◽  
pp. 987-998 ◽  
Author(s):  
Lihe Zhang ◽  
Jianwu Ai ◽  
Bowen Jiang ◽  
Huchuan Lu ◽  
Xiukui Li
Keyword(s):  
Markov Chain ◽  
Transition Probability ◽  
Saliency Detection ◽  
Absorbing Markov Chain

Region saliency detection via multi-feature on absorbing Markov chain

2015 ◽  
Vol 32 (3) ◽  
pp. 275-287 ◽  
Author(s):  
Wenjie Zhang ◽  
Qingyu Xiong ◽  
Weiren Shi ◽  
Shuhan Chen
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Absorbing Markov Chain

scholarly journals A Survey on Approaches for Saliency Detection with Visual Attention

2018 ◽  
Vol 232 ◽  
pp. 02007
Author(s):  
Qi Zhang
Keyword(s):  
Deep Learning ◽  
Visual Attention ◽  
Visual Cues ◽  
Saliency Detection ◽  
Saliency Map ◽  
Local Context ◽  
Natural Scenes ◽  
Benchmark Datasets ◽  
The Difference

Most existing approaches for detecting salient areas in natural scenes are based on the saliency contrast within the local context of image. Nowadays, a few approaches not only consider the difference between the foreground objects and the surrounding background areas, but also consider the saliency objects as the candidates for the center of attention from the human’s perspective. This article provides a survey of saliency detection with visual attention, which exploit visual cues of foreground salient areas, visual attention based on saliency map, and deep learning based saliency detection. The published works are explained and descripted in detail, and some related key benchmark datasets are briefly presented. In this article, all documents are published from 2013 to 2018, giving an overview of the progress of the field of saliency detection.

scholarly journals Salient Region Detection Using Diffusion Process with Nonlocal Connections

2018 ◽  
Vol 8 (12) ◽  
pp. 2526 ◽  
Author(s):  
Huiyuan Luo ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Yanfeng Wu
Keyword(s):  
Saliency Detection ◽  
Gaussian Model ◽  
Single Layer ◽  
Saliency Map ◽  
Detection Methods ◽  
Sparse Graph ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Benchmark Datasets

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

Sign in / Sign up

Export Citation Format

Share Document