Superpixel-based saliency detection

Li, Zhi; Meur, Le; Luo, Shuhua

doi:10.1109/wiamis.2013.6616119

Cited by 26 publications

(15 citation statements)

References 18 publications

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“…In [22], superpixel segmentation is used to compress images more efficiently than traditional techniques. Liu et al proposed to calculate the inter-superpixel similarity, global contrast, and spatial sparsity to generate a superpixel-level saliency map [23]. Superpixels are also applicable to image decomposition [24], multisensory video fusion [25], and image synthesis [26].…”

Section: A Superpixel Versus Image Patchmentioning

confidence: 99%

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SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment

Sun

Liao

Xue

et al. 2018

IEEE Trans. on Image Process.

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Full-reference image quality assessment algorithms usually perform comparisons of features extracted from square patches. These patches do not have any visual meanings. On the contrary, a superpixel is a set of image pixels that share similar visual characteristics and is thus perceptually meaningful. Features from superpixels may improve the performance of image quality assessment. Inspired by this, we propose a new superpixel-based similarity index by extracting perceptually meaningful features and revising similarity measures. The proposed method evaluates image quality on the basis of three measurements, namely, superpixel luminance similarity, superpixel chrominance similarity, and pixel gradient similarity. The first two measurements assess the overall visual impression on local images. The third measurement quantifies structural variations. The impact of superpixel-based regional gradient consistency on image quality is also analyzed. Distorted images showing high regional gradient consistency with the corresponding reference images are visually appreciated. Therefore, the three measurements are further revised by incorporating the regional gradient consistency into their computations. A weighting function that indicates superpixel-based texture complexity is utilized in the pooling stage to obtain the final quality score. Experiments on several benchmark databases demonstrate that the proposed method is competitive with the state-of-the-art metrics.

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Section: A Superpixel Versus Image Patchmentioning

confidence: 99%

“…It is necessary to segment images into a reasonable number of superpixels. A number of superpixels larger than 200 is generally sufficient for edge preservation [23]. Excessive superpixels would lead to a high computational cost.…”

Section: B Superpixel Segmentation Of Reference and Distorted Imagesmentioning

confidence: 99%

SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment

Sun

Liao

Xue

et al. 2018

IEEE Trans. on Image Process.

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“…Visual saliency is the perceptual quality that makes some objects in the scene stand out from their surrounding regions and thus capture human visual attention [3]. We, as humans, are expert at quickly and accurately identifying the most visually noticeable foreground object in the scene, known as salient object, and adaptively focus our attention on such perceived important regions [1].…”

Section: Introductionmentioning

confidence: 99%

Salient Region Extraction based on Global Contrast Enhancement and Saliency Cut for Image Information Recognition of the Visually Impaired

2018

KSII TIIS

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Saliency region extraction has become an important task and attracts much attention in numerous image processing application. Object of interest image segmentation from natural scene images for assisting the visually impaired people to access visual information is becoming more challenging research area for community. In this paper we propose a novel color contrast based saliency region extraction approach by adding pre-processing contrast enhancement technique and using saliency cut method to get a high quality foreground object mask, could be used for assistive technology system for visually impaired people. A lot of research has focused on high-quality segmentation, using color information and background or foreground prior, with surprisingly unnoticed image enhancement technique before region segmentation. Moreover, image enhancement is used to improve bad quality of picture into good picture, helpful for blind people's daily life since all randomly taken images might not be good quality. We evaluate our proposed method on the most known benchmark databases.

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“…Statistical models such as Gaussian model [40] and kernel density estimation based nonparametric model [41] are used to represent each region, and both color and spatial saliency measures of such statistical models are evaluated and integrated to measure the pixel's saliency. Using different formulations, global contrast and spatially weighted regional contrast [42], color compactness of over-segmented regions [43], distinctiveness and compactness of regional histograms [44], global contrast and spatial sparsity of superpixels [45], and two contrast measures for rating global uniqueness and spatial distribution of colors in the saliency filter [46] are exploited to generate saliency maps with well-defined boundaries. In the recently proposed hierarchical saliency model [47], saliency cues are calculated on three image layers with different scales of segmented regions, and then hierarchical inference is exploited to fuse them into a single saliency map.…”

mentioning

confidence: 99%

“…The proposed model is considerably different from [47] in the complete framework of saliency tree generation and analysis, which selects the most suitable region representation by exploiting the hierarchy of tree structure, to effectively improve the saliency detection performance. Second, on the basis of our previous work [45], [51], we integrate three measures, i.e., global contrast, spatial sparsity and object prior, at region level to reasonably initialize regional saliency measures. Third, we propose a saliency-directed region merging approach with dynamic scale control scheme for saliency tree generation, which can preserve meaningful regions at different scales.…”

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confidence: 99%

Saliency Tree: A Novel Saliency Detection Framework

Zou

Meur

2014

IEEE Trans. on Image Process.

198

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Abstract-This paper proposes a novel saliency detection framework termed as saliency tree. For effective saliency measurement, the original image is first simplified using adaptive color quantization and region segmentation to partition the image into a set of primitive regions. Then, three measures, i.e., global contrast, spatial sparsity, and object prior are integrated with regional similarities to generate the initial regional saliency for each primitive region. Next, a saliency-directed region merging approach with dynamic scale control scheme is proposed to generate the saliency tree, in which each leaf node represents a primitive region and each non-leaf node represents a nonprimitive region generated during the region merging process. Finally, by exploiting a regional center-surround scheme based node selection criterion, a systematic saliency tree analysis including salient node selection, regional saliency adjustment and selection is performed to obtain final regional saliency measures and to derive the high-quality pixel-wise saliency map. Extensive experimental results on five datasets with pixel-wise ground truths demonstrate that the proposed saliency tree model consistently outperforms the state-of-the-art saliency models.Index Terms-Saliency tree, saliency detection, saliency model, saliency map, regional saliency measure, region merging, salient node selection.

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Superpixel-based saliency detection

Cited by 26 publications

References 18 publications

SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment

SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment

Salient Region Extraction based on Global Contrast Enhancement and Saliency Cut for Image Information Recognition of the Visually Impaired

Saliency Tree: A Novel Saliency Detection Framework

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