Abstract: Techniques for detecting an event via social media content. A method includes obtaining multiple images from at least one social media source; extracting at least one visual semantic concept from the multiple images; differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images; retrieving one or more images associated with the event semantic concept signal; grouping the one or more images associated with the event semantic concept signal; annotating the group of one or more images with user feedback; and displaying the annotated group of one or more images as a visual description of the detected event.

Abstract: Techniques for detecting an event via social media content. A method includes obtaining multiple images from at least one social media source, extracting at least one visual semantic concept from the multiple images, differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images, and retrieving one or more images associated with the event semantic concept signal for presentation as a visual description of the detected event.

Abstract: A system and article of manufacture for social media event detection and content-based retrieval include obtaining multiple images from at least one social media source, extracting at least one visual semantic concept from the multiple images, differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images, and retrieving one or more images associated with the event semantic concept signal for presentation as a visual description of the detected event.

Abstract: Techniques for detecting an event via social media content. A method includes obtaining multiple images from at least one social media source, extracting at least one visual semantic concept from the multiple images, differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images, and retrieving one or more images associated with the event semantic concept signal for presentation as a visual description of the detected event.

Abstract: A system and article of manufacture for social media event detection and content-based retrieval include obtaining multiple images from at least one social media source, extracting at least one visual semantic concept from the multiple images, differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images, and retrieving one or more images associated with the event semantic concept signal for presentation as a visual description of the detected event.

Abstract: 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.

Abstract: The present invention relates to the use of search fusion methods for querying multimedia databases and more specifically to a method and system for constructing a multi-modal query of a multimedia repository by forming multiple uni-modal searches and explicitly selecting fusion methods for combining their results. The present invention also relates to the integration of search methods for content-based retrieval, model-based retrieval, text-based retrieval, and metadata search, and the use of graphical user interfaces allowing the user to form queries fusing these search methods.

Abstract: A method and apparatus for extracting a model vector representation from multimedia documents. A model vector provides a multidimensional representation of the confidence with which multimedia documents belong to a set of categories or with which a set of semantic concepts relate to the documents. A model vector can be associated with multimedia documents to provide an index of its content or categorization and can be used for comparing, searching, classifying, or clustering multimedia documents. A model vector can be used for purposes of information discovery, personalizing multimedia content, and querying a multimedia information repository.

Abstract: Systems and methods for conducting attribute-based queries over a plurality of objects using bounded memory locations and minimizing costly input and output operations are provided. A plurality of attributes are associated with each object, and a plurality of data groups, one each for the identified attributes are created. The objects associated with the attributes are placed into the appropriate data groups, and the objects contained within each data group are sorted into blocks such that each block within a given attribute contains that objects having the same attribute value. Results to the query are created by loading blocks into a primary memory location in a middleware system and combining the loaded blocks to create the desire query results. Block combinations are created based upon the fit of the given block combination to the query as expressed in an aggregation function.

Abstract: A system and method for detecting a concept from digital content are provided. A plurality of representations is generated for same data content for concept detection from the plurality of representations. A plurality of concepts is simultaneously detected from the plurality of representations of the same data content wherein at least one detector provides selection information for selecting the representations generated or a combination of the generated representations. This results in multiple instances of a representation being considered for concept detection.

Abstract: A method, system and recording medium in which descriptors at a first granularity level are propagated, mapped, and/or classified to generate an output content having descriptors at a second granularity level that is finer than the first granularity level.

Abstract: The present invention relates to the use of search fusion methods for querying multimedia databases and more specifically to a method and system for constructing a multi-modal query of a multimedia repository by forming multiple uni-modal searches and explicitly selecting fusion methods for combining their results. The present invention also relates to the integration of search methods for content-based retrieval, model-based retrieval, text-based retrieval, and metadata search, and the use of graphical user interfaces allowing the user to form queries fusing these search methods.

Abstract: A method and apparatus for extracting a model vector representation from multimedia documents. A model vector provides a multidimensional representation of the confidence with which multimedia documents belong to a set of categories or with which a set of semantic concepts relate to the documents. A model vector can be associated with multimedia documents to provide an index of its content or categorization and can be used for comparing, searching, classifying, or clustering multimedia documents. A model vector can be used for purposes of information discovery, personalizing multimedia content, and querying a multimedia information repository.