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.

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.

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.

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.

A saliency detection model based on sparse features and visual acuity

2013 ◽  
Author(s):  
Yuming Fang ◽  
Weisi Lin ◽  
Zhenzhong Chen ◽  
Chia-Wen Lin ◽  
Zhijun Fang ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Saliency Detection ◽  
Detection Model ◽  
Model Based ◽  
Sparse Features

scholarly journals Bottom-Up Saliency Detection Model Based on Amplitude Spectrum

2011 ◽  
pp. 370-380 ◽  
Author(s):  
Yuming Fang ◽  
Weisi Lin ◽  
Bu-Sung Lee ◽  
Chiew Tong Lau ◽  
Chia-Wen Lin
Keyword(s):  
Saliency Detection ◽  
Amplitude Spectrum ◽  
Bottom Up ◽  
Detection Model ◽  
Model Based

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.

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