Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along wi...Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along with negative example mining.The complexity of classifiers in the cascade is not limited,so more negative examples are used for training.Furthermore,the cascade becomes an ensemble of strong peer classifiers,which treats intraclass variation.To locally train the AdaBoost classifiers with a high detection rate,a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds.Using the aggregate channel feature(ACF),the method achieves miss rates of 35%and 14%on the Caltech pedestrian benchmark and Inria pedestrian dataset,respectively,which are lower than that of increasingly complex AdaBoost classifiers,i.e.,44%and 17%,respectively.Using deep features extracted by the region proposal network(RPN),the method achieves a miss rate of 10.06%on the Caltech pedestrian benchmark,which is also lower than 10.53%from the increasingly complex cascade.This study shows that the proposed method can use more negative examples to train the pedestrian detector.It outperforms the existing cascade of increasingly complex classifiers.展开更多
With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are d...With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax, semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine(SVM) and k-nearest neighbor(KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs. Experiments on the COAE2014(Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes.展开更多
It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this wor...It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this work, we studied how to detect artificial pornographic pictures, especially when they are on social networks. The whole detection process can be divided into two stages: feature selection and picture detection. In the feature selection stage, seven types of features that favour picture detection were selected. In the picture detection stage, three steps were included. 1) In order to alleviate the imbalance in the number of artificial pornographic pictures and normal ones, the training dataset of artificial pornographic pictures was expanded. Therefore, the features which were extracted from the training dataset can also be expanded too. 2) In order to reduce the time of feature extraction, a fast method which extracted features based on the proportionally scaled picture rather than the original one was proposed. 3) Three tree models were compared and a gradient boost decision tree (GBDT) was selected for the final picture detection. Three sets of experimental results show that the proposed method can achieve better recognition precision and drastically reduce the time cost of the method.展开更多
For the diagnosis of glaucoma,optical coherence tomography(OCT)is a noninvasive imaging technique for the assessment of retinal layers.To accurately segment intraretinal layers in an optic nerve head(ONH)region,we pro...For the diagnosis of glaucoma,optical coherence tomography(OCT)is a noninvasive imaging technique for the assessment of retinal layers.To accurately segment intraretinal layers in an optic nerve head(ONH)region,we proposed an automatic method for the segmentation of three intraretinal layers in eye OCT scans centered on ONH.The internal limiting membrane,inner segment and outer segment,Bruch’s membrane surfaces under vascular shadows,and interaction of multiple high-reflectivity regions in the OCT image can be accurately segmented through this method.Then,we constructed a novel spatial-gradient continuity constraint,termed spatial-gradient continuity constraint,for the correction of discontinuity between adjacent image segmentation results.In our experiment,we randomly selected 20 B-scans,each annotated three retinal layers by experts.Signed distance errors of?0.80μm obtained through this method are lower than those obtained through the state-of-art method(?1.43μm).Meanwhile,the segmentation results can be used as bases for the diagnosis of glaucoma.展开更多
Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-...Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.展开更多
Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical i...Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.展开更多
基金Project(2018AAA0102102)supported by the National Science and Technology Major Project,ChinaProject(2017WK2074)supported by the Planned Science and Technology Project of Hunan Province,China+1 种基金Project(B18059)supported by the National 111 Project,ChinaProject(61702559)supported by the National Natural Science Foundation of China。
文摘Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along with negative example mining.The complexity of classifiers in the cascade is not limited,so more negative examples are used for training.Furthermore,the cascade becomes an ensemble of strong peer classifiers,which treats intraclass variation.To locally train the AdaBoost classifiers with a high detection rate,a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds.Using the aggregate channel feature(ACF),the method achieves miss rates of 35%and 14%on the Caltech pedestrian benchmark and Inria pedestrian dataset,respectively,which are lower than that of increasingly complex AdaBoost classifiers,i.e.,44%and 17%,respectively.Using deep features extracted by the region proposal network(RPN),the method achieves a miss rate of 10.06%on the Caltech pedestrian benchmark,which is also lower than 10.53%from the increasingly complex cascade.This study shows that the proposed method can use more negative examples to train the pedestrian detector.It outperforms the existing cascade of increasingly complex classifiers.
基金Projects(61573380,61303185)supported by the National Natural Science Foundation of ChinaProject(13BTQ052)supported by the National Social Science Foundation of China+1 种基金Project(2016M592450)supported by the China Postdoctoral Science FoundationProject(2016JJ4119)supported by the Hunan Provincial Natural Science Foundation of China
文摘With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax, semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine(SVM) and k-nearest neighbor(KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs. Experiments on the COAE2014(Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes.
基金Projects(61573380,61303185) supported by the National Natural Science Foundation of ChinaProjects(2016M592450,2017M612585) supported by the China Postdoctoral Science FoundationProjects(2016JJ4119,2017JJ3416) supported by the Hunan Provincial Natural Science Foundation of China
文摘It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this work, we studied how to detect artificial pornographic pictures, especially when they are on social networks. The whole detection process can be divided into two stages: feature selection and picture detection. In the feature selection stage, seven types of features that favour picture detection were selected. In the picture detection stage, three steps were included. 1) In order to alleviate the imbalance in the number of artificial pornographic pictures and normal ones, the training dataset of artificial pornographic pictures was expanded. Therefore, the features which were extracted from the training dataset can also be expanded too. 2) In order to reduce the time of feature extraction, a fast method which extracted features based on the proportionally scaled picture rather than the original one was proposed. 3) Three tree models were compared and a gradient boost decision tree (GBDT) was selected for the final picture detection. Three sets of experimental results show that the proposed method can achieve better recognition precision and drastically reduce the time cost of the method.
基金Projects(61672542,61573380)supported by the National Natural Science Foundation of China
文摘For the diagnosis of glaucoma,optical coherence tomography(OCT)is a noninvasive imaging technique for the assessment of retinal layers.To accurately segment intraretinal layers in an optic nerve head(ONH)region,we proposed an automatic method for the segmentation of three intraretinal layers in eye OCT scans centered on ONH.The internal limiting membrane,inner segment and outer segment,Bruch’s membrane surfaces under vascular shadows,and interaction of multiple high-reflectivity regions in the OCT image can be accurately segmented through this method.Then,we constructed a novel spatial-gradient continuity constraint,termed spatial-gradient continuity constraint,for the correction of discontinuity between adjacent image segmentation results.In our experiment,we randomly selected 20 B-scans,each annotated three retinal layers by experts.Signed distance errors of?0.80μm obtained through this method are lower than those obtained through the state-of-art method(?1.43μm).Meanwhile,the segmentation results can be used as bases for the diagnosis of glaucoma.
基金Projects(61672542,61573380)supported by the National Natural Science Foundation of ChinaProject(2016zzts055)supported by Fundamental Research Funds for the Central Universities,China
文摘Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.
基金supported by the National Natural Science Foundation of China(Nos.61502537 and 61573380)the Hunan Provincial Natural Science Foundation of China(Nos.2018JJ3681 and 2016JJ2150)+3 种基金the Open Project Fund of Key Lab of Digital Signal and Image Processing of Guangdong Province(No.2018GDDSIPL-01)the Mutual Creation Project for Teachers and Students(No.2018gczd022)the 111 Project(No.B18059)the Fundamental Research Funds for the Central Universities of Central South University(No.2018zzts576)
文摘Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.