Telefonaktiebolaget Lm Ericsson (publ)

Joakim Söderberg

Mattias Lidström

Jonas Bjork

Jacob Ström

Tommy Arngren

Jonas Alfthan

Johan Hjelm

David Lindegren

Simon Moritz

Azadeh Bararsani

Andreas Fasbender

Apostolos Georgakis

Marika Stålnacke

Jonas Björk

Che Zhong

Jacob STRÖM

A method, medium, and apparatus are disclosed for indexing multimedia content by a computer. The method comprises segmenting the multimedia content into a plurality of segments. For each segment, the method identifies one or more features present in the segment, wherein the features are of respective media types. The method then identifies, for each identified feature in each segment, one or more respective keywords associated the identified feature. Then, the method determines, for each identified keyword associated with an identified feature in a given segment, a respective relevance of the keyword to the given segment. The respective relevance is dependent on a weight associated with the respective media type of the identified feature.

Apparatus and methods for indexing multimedia content

A method and client device is disclosed for indexing content of a multimedia file. The method comprises using a client device to segment the content of the multimedia file into a plurality of segments and to determine structure-searchable data for each segment. Determining structure searchable data for a segment comprises (1) identifying one or more features of respective multimedia types in the segment; (2) correlating each of the identified features to one or more respective keywords; and (3) calculating one or more respective relevance factors for each of the keywords, where at least one of the relevance factors is based on one or more characteristics of the client device. The method also comprises the client device transmitting the structure-searchable data (including the keywords, relevance factors, and respective media types of the identified features) to an indexing server.

Apparatus and methods for user generated content indexing

Method and a recommendation system () for making a media item recommendation to a user. A weighted graph is created () that comprises item nodes representing media items and edges linking the item nodes. Weights are also assigned to the edges based on weights of contributors shared by the linked item nodes. When an initial media item of interest to the user is identified (), the recommendation system determines () a set of media items which are linked to the initial media item in a weighted graph. At least one of the determined set of media items is also selected based on the assigned weight of edges linking the media items to the initial media item. The recommendation system then provides () the media item recommendation with the at least one selected media item to the user.

Method and apparatuses for enabling recommendations

A method in an electronic device () for handling tags associated with an acquired digital image. The method includes displaying the image and the associated tags. The tags are have been assigned relative ranking values and are selected based on the relative ranking values. A selected tag is dissociated from the image by means of the electronic device detecting a user command instructing the device to dissociate a tag from the image.

Electronic device and method for handling tags

There is described a device and method for assigning a tag to a media file associated with metadata describing features of the media file. The metadata is converted into a query feature vector associated with the media file. A weighted distance is calculated between the query vector and each of a plurality of tag feature vectors, where each tag feature vector is included in a tag instance stored in a database, each tag instance further comprising a tag and a weight vector. The weighted distance is determined by comparing each feature of the query vector with the corresponding feature in the tag feature vector to obtain an initial distance value for that feature, modifying the initial distance value by a weight for that feature obtained from the weight vector in the tag instance to obtain a comparison value for that feature, and combining the comparison values of all of the features to obtain the weighted distance. Those tag instances whose tag feature vectors have the smallest weighted distance from the query vector are selected. The user chooses one or more of the selected tags, which are then associated with the media file. The weight for each feature in the weight vectors in the tag instances selected by the tag selection unit is updated on the basis of whether or not the tag in each tag instance was chosen by the user, and the distance between the feature of the tag feature vector and the corresponding feature in the query vector.

Assigning tags to media files

The embodiments of the present invention relates to a method and system for classifying media. The classification is achieved by using annotation ontolgies and by associating bottom level concepts of the annotation ontology tree with explanatory representation data of a selected representation domain and then comparing the explanatory representation data with transformation of the media in the selected representation domain. In this way tags can be generated which corresponds to bottom level concepts of the ontology tree which corresponds to explanatory representation data which can be found in the transformed media.

Method and server for media classification

A method of providing content () associated with a weight-value, the content () previously provided to a current computer associated with a current user () that is represented by a first node () in a social network (). The method comprises the steps of: i) enabling the current computer () to display the content (), in dependence of the weight-value, ii) obtaining an input of the current user (), iii) updating the weight-value of the content (), in dependence of the input of the current user (), iv) determining a receiving computer associated with a second node () in the social network (), and v) providing the content () to the receiving computer. Corresponding computers, computer program and computer readable medium are also described.

Providing content by using a social network

A method of retrieving information comprised in a barcode is disclosed. The method comprises detecting that the barcode is present in a first image having a first image quality and capturing a first region, acquiring, when it is detected that the barcode is present, a second image having a second image quality and capturing a second region, wherein the second image quality is higher than the first image quality, and wherein the second region at least partly overlaps the first region, and decoding the barcode based on the second image to retrieve the information. A corresponding program product and a corresponding arrangement are also disclosed along with a communication device comprising the arrangement.

Method and arrangement for retrieving information comprised in a barcode

A communication apparatus for connecting to a network that requires authentication is provided. The apparatus includes a network controller for connecting to the network; a controller for controlling a connection to the network via the network controller; a sensor for obtaining biometric information of a user of the communication apparatus; and a memory for storing a subscription module applied to authentication towards the network. The subscription module includes identification information created based on biometric information of the user. In order to establish a connection to the network by use of the subscription module stored in the memory, the controller obtains biometric information of the user by use of the sensor and compares the obtained biometric information to the identification information in the subscription module.

Communication apparatus using biometrics

A method is provided for determining a correlation between a reference user and another user on the basis of two sets of ratings, where each rating is associated with a respective user. In response to a trigger, user ratings associated with the reference user and user ratings associated with the other user are collected, and all co-rated items of these two sets are correlated in the basis of an adjusted cosine correlation function which is weighted by a first and a second weighting function. The correlation is then stored and may be repeated for a plurality of users. The stored correlations may be used e.g. for ranking purposes.

Double weighted correlation scheme

In one embodiment, a method of generating annotation tags () for a digital image () includes maintaining a library () of human-meaningful words or phrases organized as category entries () according to a number of defined image description categories (), and receiving context metadata () associated with the capture of a given digital image (). The method further includes selecting particular category entries (--) as vocabulary metadata () for the digital image () by mapping the context metadata () into the library (), and generating annotation tags () for the digital image () by logically combining the vocabulary metadata () according to a defined set of deductive logic rules () that are predicated on the defined image description categories (). In another embodiment, a processing apparatus (), such as a digital processor () and supporting memory (), etc., is configured to carry out the above method, or to carry out variations of the above method.

Generation of annotation tags based on multimodal metadata and structured semantic descriptors

A method of estimating a default rating of a rated dataset is provided, where the dataset comprises at least one series of ratings associated with at least one user and each series comprise ratings associated with at least two items. For a reference user and an item for which a rated value is missing the item's average rating, i, the reference users average rating, R, and the datasets average rating, d, is collected. A Poisson distribution of the reference users rating is then generated on the basis of the reference users average rating. A random Poisson rating, u, is calculated on the basis of the Poisson distribution, and a default rating, r, is estimated by weighting the random Poisson rating on the basis of the items average rating, the users average rating and the datasets average rating.

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Solna, Sweden

Joakim Söderberg