International Business Machines Corporation

Jelena Tesic

John Richard Smith

Milind R Naphade

Apostol Ivanov Natsev

Lexing Xie

Rong Yan

Matthew Leon Hill

Shahram Ebadollahi

Chung-Sheng Li

John Ronald Kender

Ioana Roxana Stanoi

Murray Scott Campbell

Quoc-Bao Nguyen

Brian Michael Landry

A system and method for constructing a hierarchical multi-faceted classification structure includes organizing a plurality of visual categories into a multi-relational reference ontology that accounts for a plurality of different types of relationships. Media artifacts are categorized into the plurality of visual categories. The categories of artifacts are refined based on faceted ontology relationships or constraints from the multi-relational reference ontology. The multi-relational reference ontology and the one or more media artifacts with relationships are stored as the hierarchical multi-faceted classification structure in computer readable memory storage.

Multi-facet classification scheme for cataloging of information artifacts

A method and apparatus is provided to analyze social and content networks, to identify new relationships among users and content, and to more effectively link users to content. Embodiments of the invention also facilitate more relevant searches over both social and content data. One embodiment is directed to analyzing integrated social and content networks, and includes specifying a social network comprising multiple user nodes, and specifying a content network comprising multiple multimedia content nodes. Data elements associated with respective nodes of the user and content networks are analyzed, in order to detect a set of nodes comprising at least first and second user nodes and first and second content nodes, and to further detect at least three commonality links that join all the nodes of the set together. It is also determined whether a further link is a valid commonality link. The further link is initially inferred to extend between two of the nodes in the set, and to cooperatively join with the three identified commonality links to form a closed loop.

Joint analysis of social and content networks

The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage. When none of the content items of the specified content remains without a rating after the rating procedure has been completed at the given stage, ratings that have been respectively applied to at least some of the content items are selectively processed, in order to determine an overall objectionability rating for the specified content.

Method and apparatus for semantic assisted rating of multimedia content

A computer program product includes machine readable instructions for managing data items, the instructions stored on machine readable media, the product including instructions for: initializing a plurality of base models; minimizing a joint loss function to select models from the plurality for a plurality of labels associated with the data items; and at least one of sharing and combining the selected base models to formulate a composite classifier for each label. A computer system and additional computer program product are provided.

Method and apparatus for model-shared subspace boosting for multi-label classification

A method and apparatus is disclosed that receives an input narrative in a particular media format, and produces therefrom a multimedia enriched version of the input narrative. In one embodiment, a method is provided for enriching an input narrative with multimedia content, wherein the method includes the step of selectively segmenting the input narrative to form queries pertaining to different portions or elements of the input narrative. The method further comprises retrieving multimedia artifacts from one or more multimedia repositories, wherein each retrieved multimedia artifact is associated with one of the queries, and selecting a subset of the retrieved multimedia artifacts, wherein the subset includes artifacts comprising different types of multimedia content. The input narrative is then combined with respective multimedia artifacts of the subset, in order to provide an enriched and enhanced narrative.

Method and apparatus for automatic multimedia narrative enrichment

A method and apparatus is provided for automatically classifying a multimedia artifact based on scoring, and selecting the appropriate set of ontologies from among all possible sets of ontologies, preferably using a recursive routing selection technique. The semantic tagging of the multimedia artifact is enhanced by applying only classifiers from the selected ontology, for use in classifying the multimedia artifact, wherein the classifiers are selected based on the context of the multimedia artifact. One embodiment of the invention, directed to a method for classifying a multimedia artifact, uses a specified criteria to select one or more ontologies, wherein the specified criteria indicates the comparative similarity between specified characteristics of the multimedia artifact and each ontology. The method further comprises scoring and selecting one or more classifiers from a plurality of classifiers that respectively correspond to semantic element of the selected ontologies, and evaluating the multimedia artifact using the selected classifiers to determine a classification for the multimedia artifact.

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New York, NY, United States of America

Jelena Tesic