A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance

Peng, Qinmu; Cheung, Yiu-ming; You, Xinge; Tang, Yuan Yan

doi:10.1109/tsmc.2016.2564922

Cited by 30 publications

(8 citation statements)

References 48 publications

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“…In image analysis, the pixel distribution characteristic is used to smooth noises. Also, advanced techniques such as Fourier transform and wavelet transform [76][77] have been proposed. In our proposed method, we take the viewpoint of ''object separation" to smooth noises.…”

Section: Dhblan Coefficientmentioning

confidence: 99%

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Cao

Bao-zhi

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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Section: Dhblan Coefficientmentioning

confidence: 99%

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Cao

Bao-zhi

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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“…During the past few years, there are many methods attempting to segment the salient object from the saliency map of an input image [13][14][15][16][17][18][19]. The quality and effectiveness of segmentation are decided by the quality of saliency map [20]. Based on the milestone work, some structural matrix decomposition-based methods transform a saliency detection problem into a feature subspace decomposition problem, which can improve detection results in terms of both speed and accuracy.…”

Section: Related Workmentioning

confidence: 99%

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou

Liu

et al. 2018

Applied Sciences

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Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

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“…In [8] Qinmu Peng, Yiu-ming Cheung, Xinge You, and Yuan Yan Tang, presents a visual saliency detection approach, which is a hybrid of local feature-based saliency and global feature-based saliency. First, for a given input image, use an automatic selection of smoothing parameter scheme to make the image region more homogeneous.…”

Section: Figure 4: Video Saliency Detection Model [6]mentioning

confidence: 99%

A Survey on Various Saliency Detection Methods

Sebastian¹,

Daniel²

2017

IJCA

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Visual saliency is an important characteristic of Human Visual System (HVS) that select the visually significant information from scenes. The salient objects stand out relative to their neighbourhood regions. Detecting and segmenting salient objects, also known as salient object detection is used to extract the most interesting object/objects in a scene and has resulted in many applications. There are many different methods to detect saliency known as visual attention models or saliency detection methods. In past few years many saliency detection methods have been proposed. One of the main objectives of the work is to perform a detail study in the field of Saliency detection by keeping focus on the different bottom-up computational models and the methods used to predict saliency. The work aims to analyze various solutions that aid the task of HVSs properties. This paper presents various saliency detection methods.

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A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance

Cited by 30 publications

References 48 publications

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

A Survey on Various Saliency Detection Methods

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