Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection

Jian, Muwei; Qi, Qiang; Dong, Junyu; Yin, Yilong; Lam, Kin‐Man

doi:10.1016/j.jvcir.2018.03.008

Cited by 91 publications

(47 citation statements)

References 45 publications

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“…Yuan et al [25] propose a recursive pyramid network with joint attention (RPJA) to learn the significance of the fine region. In order to get rich local information, Jian et al [30] use an improved wavelet-based salient-patch detector to detect patches.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

2019

KSII TIIS

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With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of L 2,q norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

2019

KSII TIIS

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“…A large-scale underwater image database was constructed in [36]. Later, Jian et al [37] designed an underwater saliency detection model by integrating Quaternionic Distance Based Weber Descriptor (QDWD) with pattern distinctness and Local Contrast, which incorporated quaternion number system and principal components analysis simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, a plenty of saliency detection models have been designed, which have been extended to various computer-vision and multimedia applications. Among them, typical applications include object detection [4,5,8], traffic congestion analysis [7,8], facial analysis [9,10], underwater vision [36,37], health prediction [51,52,53], visual prediction [56 -61], activity recognition [62,63], image retrieval [64 -68] and so on.…”

Section: Introductionmentioning

confidence: 99%

Saliency detection based on directional patches extraction and principal local color contrast

Jian

Zhang

et al. 2018

Journal of Visual Communication and Image Representation

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Saliency detection has become an active topic in both computer vision and multimedia fields. In this paper, we propose a novel computational model for saliency detection by integrating the holistic centerdirectional map with the principal local color contrast (PLCC) map. In the proposed framework, perceptual directional patches are firstly detected based on discrete wavelet frame transform (DWFT) and sparsity criterion, then the center of the spatial distribution of the extracted directional patches are utilized to locate the salient object in an image. Meanwhile, we proposed an efficient local color contrast method, called principal local color contrast (PLCC), to compute the color contrast between the salient object and the image background, which is sufficient to highlight and separate salient objects from complex background while dramatically reduce the computational cost. Finally, by incorporating the complementary visual cues of the global center-directional map with the PLCC map, a final compounded saliency map can be generated. Extensive experiments performed on three publicly available image databases, verify that the proposed scheme is able to achieve satisfactory results compared to other stateof-the-art saliency-detection algorithms.

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“…This Visual saliency detection is one of the hot research issues in computer vision, digital signal processing, pattern recognition and etc. Generally, visual saliency detection frameworks mainly fit into two categories: one is the bottomup saliency-detection model, which is primarily engaged in employing and extracting of the underlying features in the input image/video, such as colour, texture, intensity, contrast, direction, motion [1,2].…”

Section: Introductionmentioning

confidence: 99%

Visual Saliency Detection Via Background Features and Object-Location Cues

Jian

Wang

2019

2019 25th International Conference on Automation and Computing (ICAC)

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In this paper, we propose a simple visual saliencydetection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are used to extract directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to generate a credible saliency map. Experimental results verify that the designed method is useful and effective.

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Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection

Cited by 91 publications

References 45 publications

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

Saliency detection based on directional patches extraction and principal local color contrast

Visual Saliency Detection Via Background Features and Object-Location Cues

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