Saliency detection via image sparse representation and color features combination

Zhang, Xufan; Wang, Yong; Chen, Zhenxing; Yan, Jun; Wang, Dianhong

doi:10.1007/s11042-020-09073-4

Cited by 9 publications

(12 citation statements)

References 27 publications

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“…This study has tried to incorporate a combination of different bottom-up saliency methods of pixels, and regions based on different approaches such as center-surroundedness, global contrast-based, graph-based, learning-based, and prior knowledge, as shown in Table 1. Many of these bottom-up saliency detection methods have been typically benchmarked in several studies [9,10,63,66,70,84,112,138]. In addition, we have included the RPC [66], and CNS [70] methods because of their relatedness to our method, which is not related to top-down methods.…”

Section: Methods Comparedmentioning

confidence: 99%

“…The superpixel-based segmentation is extensively used among these methods [4,8,36,68,79,80,102,106,[113][114][115]. Nevertheless, superpixel-based segmentation methods have suffered from high computational complexity because of multiple iterations and they are not adequate for diversified classes of images [68,116]. The automatic detection of region count is a difficult problem because of the diversity in color images.…”

Section: Segmentation Of Input Imagementioning

confidence: 99%

“…Salient object detection methods attempt to extract points and regions of a visual scene that are more significant to human visual attention by forming a map that defines how a region stands out from its background and analyzing image surroundings [1,8,9]. Saliency detection is extensively used to mitigate the complexity of image analysis and speed up the processing time, and it has gained popular applications in the disciplines of computer vision and artificial intelligence [8,10]. The numerous application domains of saliency include image segmentation [11][12][13][14], object detection and recognition [15][16][17], anomaly detection [18,19], image retrieval [20,21], image compression [22], object classification [23], object tracking [24], image retargeting, and summarization [25,26], alpha matting [26], target detection [27], video object segmentation [28], video summarization [29], user perceptions of digital video contents [30], and visual tracking [31].…”

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: Methods Comparedmentioning

confidence: 99%

Section: Segmentation Of Input Imagementioning

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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“…is means that only certain outermost superpixels are selected to construct the final background template B opt . More details about background optimization selection are available in our earlier work [19].…”

Section: Background Optimization Selectionmentioning

confidence: 99%

“…For instance, Zhu et al [18] proposed a robust background measure based on boundary connectivity to constrain background regions and obtained salient regions using foreground cues. Besides, in our earlier work [19], an accurate background template was first constructed, and based on it the saliency map was obtained through image sparse representation and color features combination.…”

Section: Introductionmentioning

confidence: 99%

Saliency Detection via Fusing Color Contrast and Hash Fingerprint

Yin

Zhang

Wang

2022

Journal of Electrical and Computer Engineering

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Saliency detection is a technique for automatically extracting regions of interest from the background and has been widely used in the computer vision field. This study proposes a simple and effective saliency detection method combining color contrast and hash fingerprint. In our solution, the input image is segmented into nonoverlapping superpixels, so as to perform the saliency detection at the region level to reduce computational complexity. A background optimization selection is used to construct an accurate background template. Based on this, a saliency map that highlights the whole salient region is obtained by estimating color contrast. Besides, another saliency map that enhances the salient region while restraining the background is also generated through hash fingerprint matching. Ultimately, the final saliency map can be obtained by fusing the two saliency maps. Comparing the performance with other methods, the proposed algorithm works better even in the presence of complex background or very large salient regions.

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Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

WU,

DUAN,

FAN

2024

Chinese Journal of Aeronautics

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Saliency detection via image sparse representation and color features combination

Cited by 9 publications

References 27 publications

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Saliency Detection via Fusing Color Contrast and Hash Fingerprint

Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

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