492 results about "Spectral clustering" patented technology

The invention discloses a cold-chain logistic stowage intelligent recommendation method based on spectral clustering. Scores of users for a stowage line are conveyed through a cold chain for cold-chain logistic stowage intelligent recommending, a score matrix is built, the Euclidean distance is used for calculating the user similarity, a degree matrix is used for calculating a Laplacian matrix, feature vectors are obtained by calculating feature values of the orderly Laplacian matrix, a K-means algorithm is used for clustering the feature values to obtain a user group with the similar interesting stowage line, and a stowage line is recommended inside the user group with the similar interesting stowage line, so that cold-chain logistic stowage intelligent recommending is achieved, the cold-chain logistic vehicle non-load ratio is lowered, and the profit rate of cold-chain logistic transport vehicles is increased.

The invention discloses a detection method for an abnormal behavior of a vehicle based on spectrum clustering. The detection method comprises the following steps: obtaining a space-time track of a moving target through video tracking; removing abnormality and preprocessing, thereby obtaining a normal track; constructing an image for the track, thereby obtaining an undirected image corresponding to a track sequence; calculating similarity among tracks, thereby obtaining a similarity matrix; performing Laplace transformation on the similarity matrix, thereby obtaining a Laplace matrix; clustering the feature vector matrix of the front k maximal feature values; after performing mode learning on a motion track, obtaining motion modes of the target under a normal state; if a new track meets one of the motion modes, i.e. a normal motion mode, confirming that the traffic is normal; and if not, confirming that the vehicle abnormally runs, namely, the traffic abnormality occurs. According to the detection method, through the clustering learning for the vehicle track, the monitoring for the abnormal behavior of the vehicle is realized, the abnormal lane change is detected and the basis for automation of traffic management is supplied.

The invention discloses a semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering; the segmentation process includes that: (1) the characteristics inputted to the multi-spectral sensing image are extracted; (2) N points without labels and M points with labels are randomly and evenly sampled from a multi-spectral sensing image with S pixel points to form a set n which is the summation of N and M, wherein M points with labels are used for creating pairing limit information Must-link and Cannot-link sets; (3) the sampled point set is analyzed through semi-supervised spectral clustering to obtain the class labels of the n (n=N+M) points; (4) the sampled n (n=N+M) points are used as the training sample to classify the rest (S-N-M) points through nearest-neighbor rule, each pixel point is assigned with a class label according to the class of the pixel point and is used as the segmentation result of the inputted image. Compared with prior art, the invention has good image segmentation effect, strong operability, improves the classification accuracy, avoids searching the optimum parameters through repeated test, has small limit on image size and is better applicable to the segmentation of multi-class multi-spectral sensing images.

A method summarizes a video including a sequence of frames. The video is partitioned into segments of frames, and faces are detected in the frames of the segments. Features of the frames including the faces are extracted. For each segment including the faces, a representative frame based on the features is selected. For each possible pair of representative frames, distances are determined based on the faces. The distances are arranged in a matrix. Spectral clustering is applied to the matrix to determine an optimal number of clusters. Then, the video can be summarized according to the optimal number of clusters.

The invention discloses a university library-oriented books personalized recommendation method, and solves the problems of poor large-scale data storage and query, extendibility and recommendation effect in an existing books recommendation algorithm of a university library. According to the basic thought, the method comprises the following steps of firstly, building a graph model by taking readers, books and the like in the library as nodes; secondly, converting operation log files of the readers into a reader-books category preference matrix, calculating similarity between the readers by the reader-books category preference matrix and a reader personal information matrix, and establishing an associated graph spectrum by taking operations and mined information as edges; thirdly, by combining the associated graph spectrum with spectral clustering, proposing a new books personalized recommendation model, and performing calculation to obtain class cluster distribution about the readers; and finally, when books recommendation needs to be carried out, calculating a recommended books list according to a collaborative filtering algorithm in a class cluster corresponding to a reader.

The invention discloses an Internet forum-oriented opinion leader mining method. An opinion leader mining system is involved in the method and comprises a computing center and a database server which communicates with the computing center. The method comprises the following steps of: capturing forum data by using a crawler, and improving data processing real-time property by using message-oriented middleware (MOM); extracting web page information, performing word segmentation by using a Chinese word segmentation system, and filtering spam comments by a spectral clustering method; analyzing text tendency by using an emotional corpus; setting a selection standard value of an opinion leader, and determining the opinion leader; and visualizing a result. By the method, the opinion leader in a forum can be accurately mined, and technical support is provided for related Internet public opinion supervision departments to timely find hot issues and guide the healthy development of Internet public opinions.

The invention discloses an image segmentation method based on immunity spectrum clustering, which includes: 1. extracting texture characteristic of the input image, representing each pixel point in the image with an eigenvector to obtain a characteristic set; 2. mappings the characteristic set to a linear measure space by spectrum clustering to a mapping set; 3. dividing the category number according to the given image, accidentally selecting the corresponding number of data from the mapping set as the initial clustering center, executing cloning, variation, selection and judgement in sequence, to find out a optimum clustering center with the same category number with the initial clustering center; 4. dividing all pixel points of the characteristic set to an optimum clustering center nearest to the pixel points, and giving each pixel point a category mark according to the category of optimum clustering center where the pixel point locates to complete the image segmentation. Compared with the prior technology, the invention has advantages of insensitivity to initialization, quick convergence to global optimum and high specification accuracy, which can be used in the image segmentation of SAR image processing and computer visual sense field.

The present video tracking technique outputs a Maximum A Posterior (MAP) solution for a target object based on two object templates obtained from a start and an end keyframe of a whole state sequence. The technique first minimizes the whole state space of the sequence by generating a sparse set of local two-dimensional modes in each frame of the sequence. The two-dimensional modes are converted into three-dimensional points within a three-dimensional volume. The three-dimensional points are clustered using a spectral clustering technique where each cluster corresponds to a possible trajectory segment of the target object. If there is occlusion in the sequence, occlusion segments are generated so that an optimal trajectory of the target object can be obtained.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering, relating to the technical field of image processing and mainly solving the problem of limitation of segmentation application of large-scale SAR images in the traditional spectral clustering technology. The SAR image segmentation method comprises the steps of: 1, extracting features of an SAR image to be segmented; 2, configuring an MATLAB (matrix laboratory) parallel computing environment; 3, allocating tasks all to processor nodes and computing partitioned sparse similar matrixes; 4, collecting computing results by a parallel task dispatcher and merging into an integral sparse similar matrix; 5, resolving a Laplacian matrix and carrying out feature decomposition; 6, carrying out K-means clustering on a feature vector matrix subjected to normalization; and 7, outputting a segmentation result of the SAR image. The invention can effectively overcome the bottleneck problem in computation and storage space of the traditional spectral clustering technology, has remarkable segmentation effect on large-scale SAR images, and is suitable for SAR image target detection and target identification.

The invention relates to the voltage control of the electric power field and especially relates to a reactive voltage partitioning method based on spectral clustering. A topologic matrix with a weight is used to construct a simplified power grid model. According to a spectral clustering definition, a Laplace matrix is acquired. Through an improved K-means clustering algorithm, clustering is performed on different characteristic vectors in a characteristic matrix. During clustering, modularity Q is introduced to be taken as an index of measuring an area partitioning quality. A partitioning scheme with a largest modularity Q value is selected as an initial partitioning scheme. Connectivity verification and reactive verification are performed on each area of the initial partitioning scheme. If the area can not simultaneously satisfy two conditions of area static state reactive balance and an enough reactive reserve margin, under the condition that a value of partitioning modularity Q does not change greatly, node adjusting is performed till that all the verification conditions are satisfied. In the invention, a topology structure of a complex power grid is embodied, calculating complexity is reduced, an integration evaluation system is established based on the modularity, the reactive balance and a reactive reserve index, and integration verification is performed on a partitioning result so as to ensure feasibility of the partitioning scheme.

Systems and methods are disclosed for clusterizing information by determining similarity matrix for historical information and similarity matrix for current information; generating an aggregated similarity matrix (aggregated kernel); and applying evolutionary spectral clustering on the aggregated kernel to a content stream to produce one or more clusters.

The invention discloses an automatic multi-document abstract extraction method and an automatic multi-document abstract extraction system based on sentence vectors. The automatic multi-document abstract extraction method includes S1, preprocessing document collections; S2, generating the sentence vectors through doc2vec model training; S3, cluttering the sentence vectors into sub-theme documents;S4, creating a sentence relation graph model in each sub-theme document; S5, calculating sentence weights; S6, extracting and sequencing sentences to form abstracts. The automatic multi-document abstract extraction method and the automatic multi-document abstract extraction system have the advantages that all the sentences in the target document collections are expressed by the vectors through thelarge-corpus-set training doc2vec model; sub themes are acquired through spectral clustering, one sentence is extracted from each sub theme, and accordingly, sentence redundancy is avoided; the sentences are sequenced according to positions in original documents to form the abstracts, and coherence of the abstract sentences is improved.

The invention discloses a multi-scale spectral clustering and decision fusion-based oil spillage detection method for synthetic aperture radar (SAR) images. The oil spillage detection method comprises the following steps of firstly, establishing a multi-scale object-level spectral clustering segmentation method for the SAR images based on wavelet transformation, and respectively extracting an oil spillage area or a suspected oil spillage area under different scales; secondly, identifying image segmentation results by utilizing a neural network oil spillage based on the combination of multiple indexes on a single scale, establishing a multi-scale decision fusion strategy, and fusing detection results of the single scale to complete detection and form a unified detection framework; and performing the performance evaluation of a new oil spillage identification method by taking the main performance index in an identification process as the basis. According to the multi-scale spectral clustering and decision fusion-based oil spillage detection method for the SAR images disclosed by the invention, spectral clustering-based segmentation and the identification of the oil spillage area and the suspected oil spillage area of the sea are performed under different scales by replacing elements with objects to serve basic units, and by a multi-scale decision oil spillage detection fusion algorithm, oil spillage detection is more rapid and accurate.

A Switching Kalman Filter Model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A “hidden state” models the probability of each mixture component and evolves over time in a Markov chain. Gaussian mixture models and Expectation Maximization (EM) techniques are extended for automatic spike sorting. Good initialization of EM is achieved via spectral clustering. To account for noise, the mixture model is extended to include a uniform outlier process. A greedy optimization algorithm that selects models with different numbers of neurons according to their decoding accuracy is used to automatically determine the number of neurons recorded per electrode. Closed loop neural control of external events are demonstrated using neural control of a computer curser.

The invention discloses an image segmentation method by using nucleus transmission to solve problems of large storage scale and data inconsistency in a present method. The method comprises the following steps: inputting an image, extracting color characteristics of the image, obtaining a super-pixel set of the input image by using a mean value shift method, and calculating a super-pixel color characteristic set; searching a seed point set in the super-pixel color characteristic set by using a k-means clustering method; updating a label of the seed point set by using an adaptive spectral clustering method and forming a constraint set; sending constraint information in the constraint set to whole super-pixel color feature set space by using a nucleus transmission method and obtaining a nucleus matrix; clustering the nucleus matrix by using a k- means method to obtain label vector of the super-pixel, and outputting a segmentation result. The method in the invention has the characteristics of low storage scale, maintenance of data consistency, high calculating efficiency and high segmentation precision, and can be used for object detection and tracking, medical image analysis, network image retrieval and conference video monitoring.

The invention discloses a deep learning intelligent detection method for fishing webpages, which belongs to the technical field of network information safety. The deep learning intelligent detection method comprises the following steps of (1) analyzing webpage document models to generate a webpage document feature vector F; (2) converting the webpages to be measured into images and adopting a spectral clustering method to cut the obtained images; (3) picking up characteristics of webpage images to obtain a webpage content characteristic vector N; (4) using a manifold learning Isomap algorithm to reduce dimensions of the webpage content characteristic vector N so as to obtain a characteristic space Vnew; and (5) training and testing the characteristic space Vnew by using a data base network (DBN) sorter, and judging whether the webpages to be measured are the fishing webpage or not according to results of the DBN sorter. The deep learning intelligent detection method has the advantages that measured characteristic parameter covering is comprehensive. Compared with a test characteristic extraction method, the DBN deep trust network has high detection accuracy and fast detection speed, and detection rate of fishing type attacks is improved.

The invention discloses an SAR (stop and reveres) image segmentation method based on dictionary migration clustering, which mainly solves the problems that the existing artificial mark SAR image has high cost and the existing non-mark SAR image can not assist a target SAR image in segmenting. The method has the following realization processes: 1) extracting wavelet characteristics for the target SAR image and the non-mark assistant SAR image; 2) setting circulation ending times, and preliminarily dividing the target SAR image with a k-means method; 3) training a dictionary for each class of target SAR image data; 4) migrating a group of samples for each class of target SAR image data from the assistant SAR image data; 5) removing the assistant data sample with an unstable label by a spectral clustering integration method; 6) training an assistant dictionary by each bath of purified assistant samples; and 7) updating a sample label and outputting a clustering segmenting result according to a target dictionary, the assistant dictionary and a corresponding clustering center. The SAR image segmentation method has the advantage of good segmenting effect and can be used for further identifying the SAR image target.

The invention discloses a polarization synthetic aperture radar (SAR) image classification method based on spectral clustering. The polarization SAR image classification method mainly solves the problem that an existing non-supervision polarization SAR classification method is low in accuracy. The polarization SAR image classification method comprises the steps of extracting scattering entropy H of representation polarization SAR target characteristics to serve as an input characteristic space of a Mean Shift algorithm combining with space coordination information; diving in the characteristic space with the Mean Shift algorithm to obtain M areas; choosing representation points of all areas on the M areas to serve as spectral clustering input to spectrally divide all areas, and further finishing spectral clustering on all pixel points to obtain pre-classification results; and finally classifying the whole image obtained from the pre classification with a Wishart classifier capable of reflecting polarization SAR distribution characteristics in an iteration mode to obtain classification results. Tests show that the polarization SAR image classification method is good in image classification effect and can be applied to non-supervision classification on various polarization SAR images.

The invention relates to an overlapping community discovering method based on spectral clustering and fuzzy sets. The overlapping community discovering method comprises the steps that 1, data sets ofa social network are read to generate a network structure graph, and the attribute information of nodes in the network is acquired; 2, the Jaccard coefficient and the attribute information of the nodes in the network are combined to calculate the similarity value among the nodes in the network; 3, a similarity matrix is built based on the similarity value among the nodes, and accordingly the normalized Laplacian matrix is built; 4, the feature vector and the feature value of each node are calculated, and a new feature vector is generated by utilizing methods of iteration and compression; 5, the new feature vector is orthogonalized, the membership grade is calculated, and the nodes with a plurality of high community membership grade values are subjected to division of overlapping communities; 6, the community division meeting the highest modularity requirement is selected according to the modularity divided each time; and 7, the final community division result is output. The overlappingcommunity discovering method can efficiently and accurately discover and divide the overlapping structures in the complex network.

The invention discloses a hyperspectral image classification method based on compression spectrum clustering integration. The hyperspectral image classification method comprises a classification process including the steps that (1) image characteristic sets of hyperspectral images are obtained; (2) an image characteristic set sub space subjected to dimension reduction is obtained; (3) a plurality of hyperspectral image dividing results are obtained; (4) the final hyperspectral image dividing result is obtained; (5) the hyperspectral image classification result is obtained; (6) the accurate classification of the hyperspectral image is obtained. Compared with the prior art, the hyperspectral image classification method has the advantages that the defect that a k-means algorithm adopted in the classical spectral clustering is sensitive to initialization is overcome; the characteristic dimension of the hyperspectral images is reduced; meanwhile, the classification precision is obviously improved, and the dividing effect is good.

The present invention provides a broadcast television subscriber grouping system and method based on spectral clustering integration. The system comprises: an input unit, for inputting audience preference parameters; a program database, for storing program playing information; an audience rating database, for collecting program watching information from subscribers; an audience preference space construction unit, for calling a data source from the program database and the audience rating database according to an attributive character index input by the input unit, and obtaining attributive character index data of the subscribers for the types of programs, thereby forming a preference matrix; a first grouping unit, for grouping the subscribers multiple times based on the audience preference space; a matching unit, for performing a consensus match on clusters in a grouping set by using a consensus function, so as to construct a cluster relationship diagram; a second grouping unit, for converting the cluster relationship diagram into a cluster relationship degree matrix, which is used as a similarity matrix, and for grouping the clusters by using a spectral clustering method; and an integration unit, for setting a group as a group in which a data point is located, wherein the number of occurrences of the data point in a cluster in the group is greatest.

According to the image significance detection method based on region label fusion, a super-pixel segmentation algorithm is used for preprocessing an image, and the image is segmented into a pluralityof image region blocks; Using a Gaussian kernel function to obtain region similarity, using the region similarity to perform spectral clustering of the superpixel region, obtaining a label set of image segmentation, and storing boundary information of the image according to the label set; Obtaining salient features of the image, and fusing the salient features under a conditional random field model to obtain a roughness saliency map; Utilizing the label set to propagate the boundary information, and comparing and fusing the boundary information and the roughness saliency map to obtain reconstruction of the roughness saliency map; A self-adaptive threshold segmentation mode is adopted to carry out binarization processing on the reconstructed roughness saliency map, a label indication vectoris utilized to label a saliency region into a unified label, isolated points in the saliency region are processed, and more effective saliency region detection is obtained.

The invention disclose a method for performing image segmentation by using manifold spectral clustering, which is used for solving the problems of large storage capacity and low computing efficiency and segmentation accuracy in the existing method. The method for performing the image segmentation by using the manifold spectral clustering comprises the following steps: (1) inputting an image, extracting colors and textural features of the image, and obtaining a manifold set of the input image by using a watershed algorithm; (2) computing the manifold feature set, constructing a distance matrix, and acquiring a manifold distance matrix by using the Floyd algorithm; (3) computing a similarity matrix so as to construct a degree matrix and a normalization laplacian matrix; (4) carrying out eigen-decomposition on the normalization laplacian matrix so as to construct a spectral matrix; and (5) normalizing the spectral matrix to obtain a normalization spectral matrix, acquiring the label vector of the manifold set by a K-means algorithm, and outputting a segmentation result. The method for performing the image segmentation by using the manifold spectral clustering has the advantages of small storage capacity and high computing efficiency and segmentation accuracy, and can be used for detecting focal areas of medical images, detecting defects on precision component surfaces, and processing geographic and geomorphic pictures shot by satellites.

The invention relates to an indoor WLAN signal map drawing and mapping method based on image edge detection signal correlation. The method includes the step that firstly, RSS sequences collected by a random user are utilized to construct clustering charts of received signal strength (RSS) sequences through spectral clustering; secondly, a signal logic diagram of the random user in a located object area is constructed by the utilization of the image edge detection method; next, mapping between RSS clustering nodes in the signal logic diagram and area position nodes in a physical environment diagram is established according to corresponding mapping rules; finally, position estimation on a target user is achieve by the utilization of the mapping relation between the signal logic diagram and the physical environment diagram, and meanwhile the signal logic diagram and the physical environment diagram are drawn by the utilization of drawing techniques. The method has the advantages that the readability of the diagrams is improved, and the connection relation between the nodes in the signal logic diagram and the nodes in the physical environment diagram are clearer.