Exploiting color name space for salient object detection

Lou, Jing; Wang, Huan; Chen, Longtao; Xu, Fenglei; Xia, Qingyuan; Zhu, Wei; Ren, Mengye

doi:10.1007/s11042-019-07970-x

Cited by 9 publications

(18 citation statements)

References 51 publications

(144 reference statements)

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“…Bottom-up salient object detection methods are stimulated by the human visual system and can be categorized into the eye fixation prediction (EFP) approach [61,64,65] and salient object detection (SOD) approach [4,6,[66][67][68][69][70]. The two approaches are based on the definition of saliency as "where people look" or "which objects stand out" in an image [71].…”

Section: Bottom-up Saliency Detection Methodsmentioning

confidence: 99%

“…The quantized RGB (QRGB) color image is the input to the histogram-based clustering process to reduce the number of colors in the input image. The numerous color models used in saliency detection methods include RGB [67,70,106], hue, saturation, value (HSV) [107], lightness, redness, yellowness (L*a*b*) [66,68,84,108,109] and combination of color models [67,85,106]. This study has used the QRGB color image for clustering while the L*a*b* color image was applied for the extraction of color features because of its perceptual uniformity [50,66,110].…”

Section: Methodsmentioning

confidence: 99%

“…The global color cues based on statistics and color contrast was recently utilized to overcome the inherent limitation of exploiting surroundedness cue alone [70]. Failure to detect a salient object linked to image borders is a major drawback of this method.…”

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

“…The histogram of color namespaces was utilized to measure color differences for computing the weighted attention saliency maps [70]. The saliency detection method described in [66] applied a graph-based segmentation algorithm to construct uniform regions that can preserve object boundaries more efficiently.…”

Section: Region-based Saliency Detectionmentioning

confidence: 99%

“…Inspired by this, our method adopted a grayscale morphological reconstruction with a disk-shaped structuring element of radius, to uniformly highlight the detected saliency regions while effectively suppressing background noise. Morphological reconstruction can also suppress high-intensity values of salient objects by leaving unobtrusive background regions with non-black pixels [70]. Background noise is further suppressed by computing the average intensity value of the reconstructed saliency map and then subtract this value from each pixel intensity of the saliency map to overcome the pitfall of reconstruction [127,128].…”

Section: Post-processing Of Saliency Mapmentioning

confidence: 99%

See 4 more Smart Citations

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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