251 results about "Cross media" patented technology

Social media content items and references to events that occur therein are aligned with the time-based media events they describe. These mappings may be used as the basis for sending messages to populations of authors of content items, where the populations are determined based on whether the author has written a content item that refers to a specific TV show or advertisement. TV streams are monitored to detect when and where a specific advertisement for a particular advertiser is shown. Concurrently, social media streams are monitored for content items that refer to or are about specific TV shows and advertisements. Responsive to a specific advertisement being detected as being shown during a specific TV show, a message associated with the advertisement is sent to the authors of the content items associated with that TV show or advertisement. The messages can be transmitted while the advertisement is being shown.

An apparatus and method facilitate combined query based searching with serendipitous browsing in a multimedia collection. A user selects objects to label from a local map, which may include representations of objects retrieved from the collection as being responsive to a text or image base query. The text and image portions of the object can be independently labeled. Unlabeled objects are scored and ranked based on the applied labels of labeled objects, which may take into account cross-media pseudo-relevance and user selectable (or default) parameters, such as a forgetting factor, which tends to place greater weight on more recently labeled objects, and a modality parameter, which laces greater weight on the modality (text, image, or hybrid) currently selected by the user. The local map is modified, based on the ranking, optionally after reranking of objects to improve the diversity of the displayed objects.

An experiential digitalized multi-screen seamless cross-media interactive opening teaching laboratory is integrated in testing, researching and analyzing. Experiment and data analysis are performed in a real teaching environment; under support of the multi-screen interactive technology, the laboratory comprises a laboratory functional partition, an operation support system, a data working system, an experiment information acquisition system and an audio and video input and output device; a screen jilting function among multiple mobile terminals is realized; the data working system comprises a server, a database, education resource cloud, a U-teaching system, a learning analysis and evaluation system, a mobile device, a cross-screen management module, a recording and broadcasting system and an Internet; learning space for cross-media interactive learning is provided, technologies of holographic imaging, multi-screen interaction, learning analysis and the like are integrated, and seamless fusion of the physical space and the virtual space is realized; seamless fusion of supporting technologies from formal learning to informal learning, multiple learning modes, cross-terminal, cross-media and the like is realized, and good learning experience is provided for learners.

The invention belongs to the technical field of cross-media correlation learning, and particularly relates to a cross-modal subject correction modeling method based on deep learning.The method includes two main algorithms of multi-modal file expression based on deep vocabularies and correlation subject model modeling fusing cross-modal subjection correction learning.A deep learning technology is utilized for constructing deep semantic vocabularies and deep vision vocabularies to describe a semantic description part and an image part in a multi-modal file.Based on multi-modal file expression, a cross-modal correlation subject model is constructed to model a whole multi-modal file set, so that the generation process of the multi-modal file and the correlation between different modals are described.The accuracy is high, and adaptability is high.The cross-modal subject correction modeling method has important meaning for efficient cross-media information retrieval in consideration of multi-modal semantic information on the basis of the large-scale multi-modal file (a text and an image), can improve retrieval correlation and promote user experience, and has great application value in the field of cross-media information retrieval.

The invention belongs to the field of multimedia information retrieval and relates to a method for realizing thesaurus-based query expansion and sort in image retrieval. The method comprises a WordNet-based English word semantic similarity metric algorithm, a HowNet-based Chinese word semantic similarity metric algorithm, an expansion rule-based query expansion word selection and optimization algorithm and a retrieval result evaluation and optimization algorithm. In the method, an image search engine is improved by the relevant text processing method and the relevant semantic network dictionary; and the retrieval result is sorted through semantic expansion, user interaction and improved similarity measurement. Compared with the traditional method, the method has the advantages of high accuracy rate, high integrality and low space-time cost. The method has very important significance for performing high-efficiency image retrieval according to image high-layer semantic information and on the basis of a large-scale image data set, and has wide application value in the field of cross-linguistic and cross-media retrieval.

A multimedia information retrieval system includes a storage and an electronic processing device. The latter is configured to perform a process including: computing values of a pairwise similarity measure quantifying pairwise similarity of documents of a multimedia reference repository; storing the computed values in the storage; performing an initial information retrieval process respective to the multimedia reference repository to return a set of initial repository documents; and identifying a set of top ranked documents of the multimedia reference repository based at least on the stored computed values pertaining to the set of initial repository documents.

The invention belongs to the technical field of cross-media correlation learning, and particularly discloses a deep cross-mode correlation learning-based image retrieval method for a free-hand sketch.The method comprises three main algorithms of deep multi-mode feature generation, multi-mode correlation learning modeling and similarity sorting optimization. By utilizing a deep learning technology, a depth semantic feature and a depth visual feature are constructed for describing a text tagging part and an image/sketch part in a multi-mode document. Based on a multi-mode document representation, a cross-mode correlation model is built for modeling a whole multi-mode document set, thereby describing correlation among different modes of the multi-mode document. Based on correlation featuresobtained after correlation modeling, retrieval results are sorted and optimized, and color images and texts with the maximum similarity with the queried sketch are returned.

When a user hovers a screen cursor over a TV channel icon on a cross-media bar (XMB) user interface (UI) for a threshold period, enhanced metadata from PSIP/XDS/EPG that pertains to the program currently available on the associated TV channel is presented in a pop-up window on the XMB UI.

The invention belongs to the technical field of cross-media relevance learning, and particularly relates to a social image oriented cross-modality image-label relevance learning method. The invention comprises three algorithms: multi-modal feature fusion, bidirectional relevancy measuring and cross-modality relevancy fusion; a whole social image set is described by taking a hyperimage as a basic model, the image and a label are respectively mapped into hyperimage nodes for treatment, relevancy aiming at the image and the relevancy aiming at the label are obtained, and the two different relevancies are combined according to a cross-modality fusion method to obtain a better relevancy. Compared with the traditional method, the method is high in accuracy and high in adaptivity. The method has important significance in performing efficient social image retrieval by considering multi-modal semantic information based on large-scale social images with weak labels, retrieval relevancy can be improved, user experience is enhanced, and the method has application value in the field of cross-media information retrieval.

The invention discloses an automatic page type setting method and belongs to the technical field of cross-media publishing, digital publishing, network printing and the like. In an existing type setting process, generally a manual method is adopted to set type of words and corresponding images, and the efficiency is low; or the automatic type setting is conducted on words or images separately only for the condition that the page column number is fixed, the pages are monotonous, and the complex conditions when a page contains multiple image and word elements can not be satisfied. According to the method, the words and images to be set are transformed into formative content, parameterized rectangle blocks are formed, then according to the area of the rectangle blocks, constraint information is judged, automatic type setting is conducted according to a sequencing and locating method, and in the type setting process, according to the page layout, an optimal automatic type setting result is finally obtained through a recall mode. By the adoption of the method, the matching and locating on words and images can be conducted rapidly and automatically, the accurate position and relative position relation of the words and images on the page are guaranteed, and the type setting efficiency is greatly improved.

The invention discloses a cross-media event extraction method. The cross-media event extraction method comprises the steps of setting a seed event feature library and required knowledge data; collecting a news webpage from a trusted news source and extracting a news text and metadata information; extracting event factor information from each news text and generating an initial event collection; calculating the degree of importance of each factor of an initial event in an portrayed event and generating an initial general event framework; searching a social network message text based on each factor in the initial general event framework and generating a candidate message collection; filtering the candidate messages based on the similarity of a general framework of the candidate messages and the general event framework to obtain a message queue corresponding to the initial event; and generating complete event data by using the event factors in the initial general event framework and the event factors which exist in the message queue but do not exist in the initial general framework. The cross-media event extraction method can realize accurate extraction of important events in an environment with massive cross-media data.

Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain.

The invention belongs to the cross-media information technology field and particularly is an image optimization clustering method based on the typical correlation analysis. The invention mainly adopts the typical correlation to analyze while considering content characteristics of media data in various modes, maps the characteristics of the media data in various modes to an isomorphism sub-space of a united dimension through the sub-space mapping algorithm and obtains the final clustering result through optimizing clustering algorithm. The invention overcomes single-mode characteristic limitation in the multimedia field where only data is used, effectively solves the isomerism problem of the media data in various modes on the bottom layer characteristics, realizes the united measurement of the media object information between various modes, obtains results which are more accurate, more effective and more comforted to the needs in the large scale image data and has a wide application value in the cross-media processing and the retrieval field.

Recommendations for a media item associated with a primary entity are based on co-interaction information gathered from other media content items of several different media types that are also associated with the primary entity. Co-interaction information can include, for example, co-click data for websites, co-watch data for videos, or co-purchase data for purchases. The co-interaction data is processed to determine a co-interaction score between primary media items and secondary media items. From the co-interaction scores, secondary entities associated with the secondary media items are determined. A relatedness score is determined for these secondary entities based on the aggregation of the co-interaction scores of the secondary media items they are associated with. The relatedness score indicates a determination of how related one entity is to another. The secondary entities are ranked according to relatedness score in order to determine secondary entities most relevant to the primary entity.

The invention belongs to the technical field of cross-media information retrieval and particularly relates to face and name aligning method and system based on image characteristics and text content in cross media news retrieval. In the invention, four main algorithms are included, and are name importance assessment algorithm, multimode information discovery algorithm based on web excavation, face set cohesion algorithm and multimode aligning combination optimization algorithm. In the invention, the related image characteristics and text content processing method is used, meanwhile, relative mathematical model is built, optimization to new picture search is performed, and through multi-grade and deep-level text content analyses and effective face-name alignment evaluation mechanism, and combination optimization at the aim of problems can be achieved. According to the invention, a great significance to efficient image retrieval performed under the consideration of high level semantic information of images and on the basis of large-scale and multifarious new image can be played, the retrieval relativity can be enhanced, the user experience is enhanced, and the wide application value is played in the field of medium information retrieval.

The invention discloses a multi-dimensional geographic scene identification method fusing geographic region knowledge. The method comprises the steps of preprocessing images in a database to obtain satisfied geographic scene images; obtaining object region image blocks by utilizing a method for quickly searching for object regions in the images; pre-training the obtained object region image blocks of the geographic images by using a deep convolutional neural network, performing an accurate adjustment process until the performance of the deep convolutional neural network of the scene images is no longer improved, and fusing feature matrixes into output eigenvectors; pre-establishing a geographic entity noun keyword dictionary by acquired entity noun data in geographic scene classification, performing word segmentation on target identification result data to obtain key words in a target identification result, and establishing text features; and fusing the text features and multi-dimensional image features into eigenvectors as inputs, realizing cross-media-data identification classification, and realizing scene classification fusing geographic entity information.

The invention provides an information browsing and retrieval method based on a semantic entity-relationship model and visualized recommendation, comprising the following steps: first collecting data from the internet at regular time, then extracting the semantic entity and relationship, converting the obtained data into the original semantic entity-relationship model Dr and adding the original semantic entity-relationship model Dr into a historical database after time delay, generating a user knowledge model KU presenting the known knowledge of the user after the data in the historical database and a learning/forgetting curve of the user are subjected to convolution and using the user knowledge model KU to predict the data in the original semantic entity-relationship model Dr. The method has the following advantages: 1. the users can check the information which the users are interested in; 2. relatively reasonable recommendation can be obtained without any input; 3. both the written information and the multimedia information such as videos, images and the like can be inquired, and cross-media inquiry is also available; and 4. the unstructured information can be checked intuitively.

The invention relates to a cross-media retrieval method based on deep learning and consistent expression spatial learning. By starting with two methods including feature selection and the similarity estimation of two highly-isomerous feature spaces, the invention puts forward the cross-media retrieval method capable of improving multimedia retrieval accuracy to a large extent by aiming at the cross-media information of two modalities including an images and a text. The method disclosed by the invention is a multimedia information mutual retrieval method which aims at two modalities including the image and the text, and cross-media retrieval accuracy is improved to a large extent. In a model which is put forward by the invention, a regulated vector inner product is adopted as a similarity metric algorithm, the directions of the feature vectors of two different modalities are considered, the influence of an index dimension is eliminated after centralization is carried out, an average value of elements is subtracted from each element in the vectors, the correlation of the two vectors subjected to average value removal is calculated, and accurate similarity can be obtained through calculation.

The invention discloses a teaching method and system based on cross-media dynamic knowledge graph. The method comprises the following steps of constructing the cross-media dynamic knowledge graph; storing entity related information in the knowledge graph, and returning attribute values corresponding to entities through subject knowledge questions and answers; obtaining feedback information of students and knowledge points, calculating the knowledge point recommendation degree and the learning resource recommendation value score, and therefore, constructing a knowledge point recommendation listand a learning resource recommendation list and returning the lists to learners; constructing a course knowledge card; recommending friends, and recommending other users with similar learning progress and learning conditions to the current user. The teaching system comprises a data layer, a data analysis layer, an application layer and a user layer. The teaching method can be realized through a computer-readable storage medium. The problem that in the prior art, a teaching scheme cannot be formulated in a targeted mode according to learning characteristics is solved, the individualized teaching can be achieved, and the teaching level is effectively improved.

The invention discloses a management method of media placement and a system of the media placement. The management method of the media placement comprises a step of gaining a plurality of platform data of the media; a step of analyzing a transferring parameter according to the platform data; a step of conducting adjustment towards advertisement-placing strategies according to the transferring parameter. A cross-media overall-situation compiling and placing of network marketing can be achieved and optimized steps of each media platform advertisement can be integrated in a whole. The system has the advantages of being high in working efficiency and saving personnel cost.

Indexing, searching, and retrieving the content of speech documents (including but not limited to recorded books, audio broadcasts, recorded conversations) is accomplished by finding and retrieving speech documents that are related to a query term at a conceptual level, even if the speech documents does not contain the spoken (or textual) query terms. Concept-based cross-media information retrieval is used. A term-phoneme/document matrix is constructed from a training set of documents. Documents are then added to the matrix constructed from the training data. Singular Value Decomposition is used to compute a vector space from the term-phoneme/document matrix. The result is a lower-dimensional numerical space where term-phoneme and document vectors are related conceptually as nearest neighbors. A query engine computes a cosine value between the query vector and all other vectors in the space and returns a list of those term-phonemes and/or documents with the highest cosine value.