A Brief Survey of Low-Level Saliency Detection

Wang, Lei; Dong, Shuilong; Li, Haisheng; Zhu, Xiaobin

doi:10.1109/isai.2016.0130

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…The leading models of visual saliency may be organized into three stages: extraction, activation, normalization. Wang et al [34] surveyed the corresponding literature on the low-level methods for visual saliency. It is needed to mention the studies conducted by Bruce et al [35] on the saliency, visual attention and visual search processes.…”

Section: ) Traditional Approaches-based Saliency Detectionmentioning

confidence: 99%

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

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In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

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Section: ) Traditional Approaches-based Saliency Detectionmentioning

confidence: 99%

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

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“…However, object interior suppression, boundary-blurring, and poor object segmentation are their main shortcomings. Hence, pixel-based methods are seldom explored in recent times [96]. In contrast, the region-based methods cluster an image into an abstract representation of homogenous regions and perform saliency computation by contrasting region pairs [56,66,95].…”

Section: Pixel-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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“…The contrastprior is a very crucial feature and one of the most used priors. Concretely, the contrast priors comprise of local-contrast prior and global-contrast prior, and the contrast-prior assumes that the salient-regions are always dissimilar from their neighborhoods or scenes [40]. Beside contrast-priors, location priors consist of center-priors and background-priors.…”

Section: Saliency Detection Modelsmentioning

confidence: 99%

A brief survey of visual saliency detection

Ullah

Jian

Hussain

et al. 2020

Multimed Tools Appl

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Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes. This field has many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models proposed in this field, it still has a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. We have discussed and reviewed its co-related fields, such as Eyefixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper. PISA[71] CVPR 2013 SD+CP NA GR[72] SPL 2013 GC+CS M PCA[73] CVPR 2013 GC C++ COV[74] JOV 2013 LC+CS M RBD[18] CVPR 2014 BC M SLF[75] CVPR 2014 F+ B M PDE[68] CVPR 2014 CP+B+CLP NA ILP[67] ITOIP 2015 GC+SD NA

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A Brief Survey of Low-Level Saliency Detection

Cited by 4 publications

References 21 publications

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

A brief survey of visual saliency detection

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