Performance Comparison of Saliency Detection

Li, Ning; Bi, Hongbo; Zhang, Zheng; Kong, Xiaoxue; Lu, Di

doi:10.1155/2018/9497083

Cited by 12 publications

(12 citation statements)

References 52 publications

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“…In addition, the DSR method consistently achieved lower MAE for all image categories, except for the category of multiple objects and the ECSSD. Superior methods that exploited the principle of center prior can exclude salient objects that touch the image boundary because salient objects are not always located at the image center [47]. However, the proposed method still managed to demonstrate outstanding performance on boundary images with the proper integration of color contrast, contrast ratio, spatial features, and center prior.…”

Section: Discussionmentioning

confidence: 99%

“…The global contrast methods alleviate the problem of attenuated object saliency values of local contrast methods, but highlighting salient regions uniformly is still a delinquent they are facing. The incorrect highlighting of background region than the salient object is another drawback of global contrast-based methods, especially for images with complex backgrounds or large salient objects [47].…”

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

“…Suitable prior knowledge can enhance the quality of saliency detection, but the ultimate results are not absolute on images with complex background and foreground objects that possess variable shapes, sizes, locations, and appearances. The center prior methods are not sufficient to trace salient objects when the image background is framed near the image center or salient objects are close to the image boundary [36,47,59,79]. The methods of exploiting background and connectivity priors have suffered from incorrect suppression of salient objects that touch image boundary [6,32,79,80].…”

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

“…The bottom-up approach is data-driven without semantic information but grounded in the connotation of primitive features such as color, intensity, shape, and texture that are simple to implement [32,46,47]. The bottom-up methods compute uniqueness in primitive features of image pixels and surrounding regions.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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“…Saliency detection has powerful application in computer vision field. Li et al [9 ] performed regional salient detection of foreground for contrast classification of a priori background. Patel et al [10 ] detected mamogram masses using MHAT method.…”

Section: Introductionmentioning

confidence: 99%

Robust spatial information based tumour detection for brain MR images

2020

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Multimodal brain MR image analysis is still a challenging research area due to its complex intensity distribution and sensitivity towards the noise. Tumourous cells have different characteristics than normal human cells, which makes them more salient. In this Letter, the authors propose a novel unsupervised spatial information based saliency boosting tumour detection method which will help to identify tumourous cells by making it more clearly visible. Initially, a pseudo‐coloured MR image is formed using the CIELab colour space. Saliency map has been established by calculating distance among scales varying elliptical windows in both spatial and colour space. Elliptical windows endeavour to cover‐up curved outliers of the brain images. The average intensity value is kept constant by fixing the axis ratio for each window. The proposed algorithm has been evaluated on both real and simulated brain images of different patients from MICCAI‐BRATS database. The performance analysis of the new algorithm exhibits higher accuracy with a low computational complexity as compared to other state of the art. The efficacy is due to the immobility of the window across rows and columns to move over the image. The novelty of the proposed technique is that neither it downscales the input images nor require any training bases.

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