A Novel Probabilistic Contrast-Based Complex Salient Object Detection

Singh, Surya Kant; Srivastava, Rajeev

doi:10.1007/s10851-019-00882-3

Cited by 4 publications

(2 citation statements)

References 62 publications

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“…To prove the effectiveness of the proposed method, we compared our method with two other commonly used visual saliency models. They are the Luminance Contrast [63,64] (LC) method and Spectral Residual (SR) method [32,65]. The LC algorithm is an effective method to calculate a spatial saliency map using image color statistics.…”

Section: Comparison With Other Visual Saliency Modelsmentioning

confidence: 99%

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

Luo,

Chen,

Wang

et al. 2023

Remote Sensing

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For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.

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Section: Comparison With Other Visual Saliency Modelsmentioning

confidence: 99%

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

Luo,

Chen,

Wang

et al. 2023

Remote Sensing

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show abstract

“…Saliency-related detections approaches are divided into top-down method and bottom-up method [10]. The top-down methods are based on human experiences of visual saliency, and the bottom-up methods are based on the capability of extracting salient features in tagged images.…”

Section: Introductionmentioning

confidence: 99%

Salient Object Detection: Integrate Salient Features in the Deep Learning Framework

Chen

Shang

et al. 2019

IEEE Access

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Salient object detection in complex environments brings the challenge from the collections of large number of training images for deep learning algorithm. It is difficult to collect the enough number of training data for varied salient objects in different scenes, and furthermore the salient objects are usually compared with the background. This paper proposes a novel method to integrate the salient features into the deep learning framework, and design a parallel multi-scale structure of the neural network to enhance the ability to detect salient objects. In addition, a multiple stage method is proposed to optimize the training process and make the datasets more prominent in the commonality of the salient features, which effectively reduces the difficulty of correctly distinguishing salient objects in complex environments. Experiments show that the proposed approach enhances the ability of neural networks to learn specified features and improves the detection effect of salient objects in complex scenes. INDEX TERMS Salient object detection, salient features, deep learning, parallel multi-scale structure.

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A robust RGBD saliency method with improved probabilistic contrast and the global reference surface

Singh

Srivastava

2021

Vis Comput

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A Novel Probabilistic Contrast-Based Complex Salient Object Detection

Cited by 4 publications

References 62 publications

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

Salient Object Detection: Integrate Salient Features in the Deep Learning Framework

A robust RGBD saliency method with improved probabilistic contrast and the global reference surface

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