Salient Region Detection Using Diffusion Process with Nonlocal Connections

Luo, Huiyuan; Han, Guangliang; Liu, Peixun; Wu, Yanfeng

doi:10.3390/app8122526

Cited by 5 publications

(5 citation statements)

References 40 publications

(165 reference statements)

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“…Furthermore, in order to increase the diversity of the background and restrain the background regions, a small number of random non-edge background nodes are selected to form a new edge node set and a background-based absorbing node set. Moreover, to obtain more homogeneous salient regions, we design the non-local connection similar to [ 47 ]. Next, we will introduce the construction process of the background seed screening mechanism and the non-local connections in detail.…”

Section: Methodsmentioning

confidence: 99%

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Han

Liu

et al. 2021

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Han

Liu

et al. 2021

Sensors

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“…The method [36] incorporated compactness cues and local contrast with a diffusion process using manifold ranking to lessen the constraint of local contrast that highlights the boundaries of objects rather than the entire region. However, consideration of local relevance among neighboring regions can lead to incorrect suppression of salient regions, especially in images with heterogeneous salient object features [76]. A local contrast-based method for detecting small targets by computing contrast between the targeted small regions and surrounding regions was proposed [77].…”

Section: Local Contrast-based Saliency Detectionmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

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“…Saliency analysis is an effective technique for detecting ROIs because it can rapidly and accurately divide an image into foreground targets and background regions [26]. Various saliency analysis models have emerged.…”

Section: Mhft Saliency Modelmentioning

confidence: 99%

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Guo

Yuan

2020

Sensors

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Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.

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Salient Region Detection Using Diffusion Process with Nonlocal Connections

Cited by 5 publications

References 40 publications

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

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