Saliency detection using quatemionic distance based weber descriptor and object cues

Jian, Muwei; Qi, Qiang; Dong, Jiwen; Sun, Xin; Sun, Yu; Lam, Kin‐Man

doi:10.1109/apsipa.2016.7820767

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…Unlike local methods, which are sensitive to high frequency image contents like edges and noise, global methods are less effective when the textured regions of salient objects are similar to the background. In order to reduce the effect of background and generate saliency maps with little noise, QDWD, which was initially proposed to detect the outliers and edges in an image [31], is utilized to represent the global shape information for the HVS to detect saliency [19,35]. A quaternion q is made up of one real part and three imaginary parts, as follows: D q q using the method in [31].…”

Section: Quaternionic Distance Based Weber Descriptormentioning

confidence: 99%

“…2(b) show the fusion of the different feature maps to form an integrated global shape map, which can be utilized for saliency detection. More details about QDWD features for saliency detection, please refer to [19]. Fig.…”

Section: Quaternionic Distance Based Weber Descriptormentioning

confidence: 99%

“…Jian et al [18] proposed a visual-attention-aware model to mimic the human visual system (HVS) for salient-object detection. In [19], a bottom-up saliencydetection method was proposed by integrating directional, center and color cues. In [20], a novel self-paced multiple-instance learning (SP-MIL) framework, which considers both multiple instance learning (MIL) and self-paced learning (SPL), was proposed for co-saliency detection.…”

Section: Introductionmentioning

confidence: 99%

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Saliency Detection via Combining Global Shape and Local Cue Estimation

Jian

Yin

et al. 2017

Lecture Notes in Computer Science

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Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliencydetection algorithms.

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Section: Quaternionic Distance Based Weber Descriptormentioning

confidence: 99%

Section: Quaternionic Distance Based Weber Descriptormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Saliency Detection via Combining Global Shape and Local Cue Estimation

Jian

Yin

et al. 2017

Lecture Notes in Computer Science

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“…As an extension of image saliency in the field of video, video saliency [16,17] has also received extensive attention. According to the features, the current video saliency detection methods can be divided into three categories: methods based on space domain [18], methods based on time domain [19,20] and methods based on spatiotemporal domain [21].…”

Section: Introductionmentioning

confidence: 99%

Strong target constrained video saliency detection

Han

Liu

2019

SN Appl. Sci.

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Aiming at the problem of video saliency detection, a strong target constrained video saliency detection method is proposed in this paper. In order to detect the salient region fast and effectively, strong target constraints forcing by the location, scale and color model are introduced into the video saliency detection. First, a target locating strategy for obtaining the location and scale information is proposed by correcting the result of video tracking with the optical flow result and the segmentation result of the last frame. Second, the estimated color model of the target is also calculated by the obtained segmentation results. Finally, the strong target constraints are integrated into the saliency model in the way of extending the significance hypothesis, and a high quality saliency map is obtained, where segmentation is employed for constrained parameters updating. In details, Densecut is initialized by the obtained saliency map to calculate the segmentation result of the last frame. Compared with some state-of-art saliency detection methods, our proposed method performs outstandingly, and the results on DAVIS dataset are significantly improved in terms of accuracy and robustness.

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“…In [24], a visual-attention-aware model was proposed for salient-object detection. Recently, a bottom-up saliency-detection method by integrating Quaternionic Distance Based Weber Descriptor (QDWD), center and color cues, was presented in [25]. In [26], a novel and effective deep neural network method incorporating low-level features was proposed for salient object detection.…”

Section: Introductionmentioning

confidence: 99%

Saliency Detection Using Texture and Local Cues

Jian

Yin

et al. 2017

Communications in Computer and Information Science

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In this paper, a simple but effective method is proposed for detecting salient objects by utilizing texture and local cues. In contrast to the existing saliency detection models, which mainly consider visual features such as orientation, color, and shape information, our proposed method takes the significant texture cue into consideration to guarantee the accuracy of the detected salient regions. Firstly, an effective method based on selective contrast (SC), which explores the most distinguishable component information in texture, is used to calculate the texture saliency map. Then, we detect local saliency by using a locality-constrained linear coding algorithm. Finally, the output saliency map is computed by integrating texture and local saliency cues simultaneously. Experimental results, based on a widely used and openly available database, demonstrate that the proposed method can produce competitive results and outperforms some existing popular methods.

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Saliency detection using quatemionic distance based weber descriptor and object cues

Cited by 9 publications

References 14 publications

Saliency Detection via Combining Global Shape and Local Cue Estimation

Saliency Detection via Combining Global Shape and Local Cue Estimation

Strong target constrained video saliency detection

Saliency Detection Using Texture and Local Cues

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