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Saliency Detection via manifold ranking Based on Robust Foreground

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International Journal of Automation and computing 2021年第1期18卷 73-84页

作者： Wei-Ping Ma Wen-Xin Li Jin-Chuan Sun Peng-Xia Cao Lanzhou Institute of Physics China Academy of Space TechnologyLanzhou 730000China

The graph-based manifold ranking saliency detection only relies on the boundary background to extract foreground seeds,resulting in a poor saliency detection result,so a method that obtains robust foreground for manifold ranking is proposed in this ***,boundary connectivity is used to select the boundary background for manifold ranking to get a preliminary saliency map,and a foreground region is acquired by a binary segmentation of the ***,the feature points of the original image and the filtered image are obtained by using color boosting Harris corners to generate two different convex *** the intersection of these two convex hulls,a final convex hull is ***,the foreground region and the final convex hull are combined to extract robust foreground seeds for manifold ranking and getting final saliency *** results on two public image datasets show that the proposed method gains improved performance compared with some other classic methods in three evaluation indicators:precision-recall curve,F-measure and mean absolute error.

关键词： Saliency detection manifold ranking boundary connectivity convex hull robust foreground

Syndrome Classification Based on manifold ranking for Viral Hepatitis

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Chinese Journal of Integrative Medicine 2014年第5期20卷 394-399页

作者：赵玉凤何丽云刘保延李筠李丰衣霍蕊莉景向红 Clinical Evaluation Center Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical Sciences China Academy of Chinese Medical Sciences Infectious Disease Clinics and Research Centers People's Liberation Army 302Hospital Institute of Acupuncture and Moxibustion China Academy of Chinese Medical Sciences

Treatment determination based on syndrome differentiation is the key of Chinese medicine. A feasible way of improving the clinical therapy effectiveness is needed to correctly differentiate the syndrome classifications based on the clinical manifestations. In this paper, a novel data mining method based on manifold ranking （MR） is proposed to explore the relation between syndromes and symptoms for viral hepatitis. Since MR could take the symptom data with expert differentiation and the symptom data without expert differentiation into the task of syndrome classification, the clinical information used for modeling the syndrome features is greatly enlarged so as to improve the precise of syndrome classification. In addition, the proposed method of syndrome classification could also avoid two disadvantages in previous methods： linear relation of the clinical data and mutually exclusive symptoms among different syndromes. And it could help exploit the latent relation between syndromes and symptoms more effectively. Better performance of syndrome classification is able to be achieved according to the experimental results and the clinical experts.

关键词： Chinese medicine syndrome classification data mining manifold ranking

Saliency Rank:Two-stage manifold ranking for salient object detection

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Computational Visual Media 2015年第4期1卷 309-320页

作者： Wei Qi Ming-Ming Cheng Ali Borji Huchuan Lu Lian-Fa Bai Nanjing University of Science and Technology Nankai University University of Wisconsin Dalian University of Technology

Salient object detection remains one of the most important and active research topics in computer vision,with wide-ranging applications to object recognition,scene understanding,image retrieval,context aware image editing,image compression,etc. Most existing methods directly determine salient objects by exploring various salient object ***,we propose a novel graph based ranking method to detect and segment the most salient object in a scene according to its relationship to image border(background) regions,i.e.,the background ***,we use regions/super-pixels as graph nodes,which are fully connected to enable both long range and short range relations to be modeled. The relationship of each region to the image border(background) is evaluated in two stages:(i) ranking with hard background queries,and(ii) ranking with soft foreground queries. We experimentally show how this two-stage ranking based salient object detection method is complementary to traditional methods,and that integrated results outperform both. Our method allows the exploitation of intrinsic image structure to achieve high quality salient object determination using a quadratic optimization framework,with a closed form solution which can be easily *** method evaluation and comparison using three challenging saliency datasets demonstrate that our method consistently outperforms 10 state-of-theart models by a big margin.

关键词： salient object detection manifold ranking visual attention saliency

Robust interactive image segmentation via graph-based manifold ranking

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Computational Visual Media 2015年第3期1卷 183-195页

作者： Hong Li Wen Wu Enhua Wu Department of Computer and Information Science University of Macao Chinese Academy of Sciences

Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.

关键词： interactive image segmentation graph structure graph edge weights manifold ranking relevance inference

A novel classification method for aid decision of traditional Chinese patent medicines for stroke treatment

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Frontiers of Medicine 2017年第3期11卷 432-439页

作者： Yufeng Zhao Bo Liu Liyun He Wenjing Bai Xueyun Yu Xinyu Cao Lin Luo Peijing Rong Yuxue Zhao Guozheng Li Baoyan Liu Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences Beijing 100700 China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing 100700 China Qingdao Hiser Hospital Qi_n_gdao 266033 China Institute of Acupuncture and Moxtbustton China Academy of Chinese Medical SciencesBeijing 100700 China China Academy of Chinese Medical Sciences Beijing 100700 China

Traditional Chinese patent medicines are widely used to treat stroke because it has good efficacy in the clinical environment. However, because of the lack of knowledge on traditional Chinese patent medicines, many Western physicians, who are accountable for the majority of clinical prescriptions for such medicine, are confused with the use of traditional Chinese patent medicines. Therefore, the aid-decision method is critical and necessary to help Western physicians rationally use traditional Chinese patent medicines. In this paper, manifold ranking is employed to develop the aid-decision model of traditional Chinese patent medicines for stroke treatment. First, 115 stroke patients from three hospitals are recruited in the cross-sectional survey. Simultaneously, traditional Chinese physicians determine the traditional Chinese patent medicines appropriate for each patient. Second, particular indicators are explored to characterize the population feature of traditional Chinese patent medicines for stroke treatment. Moreover, these particular indicators can be easily obtained by Western physicians and are feasible for widespread clinical application in the future. Third, the aid-decision model of traditional Chinese patent medicines for stroke treatment is constructed based on manifold ranking. Experimental results reveal that traditional Chinese patent medicines can be differentiated. Moreover, the proposed model can obtain high accuracy of aid decision.

关键词： traditional Chinese patent medicines stroke aid decision data mining manifold ranking