36 results about "Probabilistic latent semantic analysis" patented technology

The invention provides a labelling image scene clustering method based on vision and labelling character related information. The method comprises the following steps of: dividing a training image and a test image respectively by using a NCut (Normalized Cut) image dividing algorithm; constructing a vision nearest-neighbour graph G(C)(V, E) of all images {J1, ., Jl} PCtrain for learning, wherein in a training image set, each image has one group of initial normalized labelling character weight vectors; spreading the labelling character of each training image among the vision nearest neighbours, receiving the accepted images according to the degree of normalized EMD (Earth Mover's Distance) among the accepted images; for each training image, normalizing the accumulated labelling character weights; after the vision characteristics of the image are converted into a group of labelling characters with weights, carrying out the scene semantic clustering by using a PLSA (Probabilistic Latent Semantic Analysis) model; learning each scene semantic vision space by using a Gaussian mixture model; and carrying out the scene classification by using the vision characteristics. With the invention, the coupling precision between the vision characteristics of the image and the labelling character can be increased, and the method can be directly used for the automatic semantic labelling of the image.

The invention discloses a land cover classification method and system based on the spatial clustering of crowd-source geographic data; the crowd-source geographic data is obtained, and the crowd-source geographic data is used as the land cover classification data; The coordinate information of the spatial position is used to carry out the spatial clustering of the data points, and the data points are clustered into several groups of data points; the several groups of data points obtained by the spatial clustering are used to delineate the land cover area containing each group of data points; The text information in each land cover area is input into the probabilistic latent semantic analysis model, and the probabilistic latent semantic analysis model outputs the land cover topics and topic weights composed of the words in the text information in each land cover area, and filters the text information in the land cover area The land cover type corresponding to the topic with the highest weight is used as the basis for judging the land cover type, and the land cover type is judged for the land cover area to be detected according to the basis for judging the land cover type.

The present application proposes a document topic mining method and apparatus. The method comprises: according to a preset topic mining number, performing loop iteration processing on information in at least one received document based on a probabilistic latent semantic analysis model, and acquiring a posteriori estimate of each topic implied by each sentence in each document; according to the posteriori estimate of each topic, acquiring a membership weight of each word in each topic in each sentence; and generating a topic set corresponding to the topic mining number, wherein each topic set comprises a word related to each topic and screened out according to the membership weight of each word in each topic in the sentence. According to the document topic mining method and apparatus provided by the present application, the document topic is more comprehensively and accurately mined based on a PLSA (Probabilistic Latent Semantic Analysis) algorithm, and the correlation of document topic content is improved, thereby enabling a result of a search engine to be closer to semantic information of the document.

The invention discloses an image marking method based on semi-supervised subject modeling. The method comprises the following steps of: firstly, obtaining images from the Internet, including images with text marks and unmarked images; then, modeling the relation between the visual features and text marks of all images through latent subjects by use of a model similar to probabilistic latent semantic analysis; establishing the nearest-neighbor graphs of all images, and adjusting the model according to the manifold structure obtained by modeling the nearest-neighbor graphs; learning the model by an expectation maximization algorithm, and calculating the probability of matching the latent subjects with the images respectively; and finally, calculating the probability of matching each text mark with the unmarked images according to the probability of matching the latent subjects with the images, and selecting the text mark with the highest probability to mark the unmarked images.

The invention discloses a method and a device for realizing navigation website and belongs to the technical field of internet. The device comprises a website description information generation unit, a client party visitor description information generation unit, a probabilistic latent semantic analysis (PLSA) unit, a comprehensive treatment unit, a display output unit and a collection unit. By adoption of the technical scheme, the website which the client party visitor is interested in and tends to visit can be recommended to the client party visitor.

The invention discloses a Web service discovery method based on a probabilistic latent semantic analysis model, wherein the probabilistic latent semantic analysis model is utilized to perform modeling analysis on analyzed Web service descriptive documents, semantic concepts hidden behind service description are dug out for semantic clustering, demand service and service-centralized service are subjected to similarity matching at the relatively advanced concept hierarchy, and in combination with spectral clustering on semantic hierarchy, a service data set is subjected to irrelevant data filtration by a spectral clustering-based algorithm prior to the semantic clustering, thereby compressing the computation complexity. As proved by tests, the method is quite superior in both precision ratio and recall ratio of service discovery.

The invention discloses a recommended method for analyzing the latent semantic problem based on the probability. The method describes the interest of a user through a state model in the latent semantic analysis on the probability and provides the compatible problem recommendation for the user interactive type question-answering system. The method adopts a ternary state model, has the advantages of two recommended ways based on the content and collaborative filtering, performs problem recommendation according to the personalized information of the user, and has high accuracy and good applicability in the user interactive type question-answering system.