Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learn...Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.展开更多
Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various det...Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.展开更多
In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploit...In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.展开更多
基金This work is supported by the National Natural Science Foundation of China[grant number 61801336]the China Postdoctoral Science Foundation[grant number 2019M662717 and 2017M622521]the China Postdoctoral Program for Innovative Talent[grant number BX201700182].
文摘Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.
基金supported in part by the National Science Fund for Distinguished Young Scholars under grant no.61925112,in part by the National Natural Science Foundation of China under grant no.61806193 and grant no.61772510Support Program of Shaanxi under grant no.2020KJXX‐091in part by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences under grant no.QYZDY‐SSW‐JSC044.
文摘Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.
文摘In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.
