Abstract: According to one implementation, a media content annotation system includes a computing platform including a hardware processor and a system memory storing a model-driven annotation software code. The hardware processor executes the model-driven annotation software code to receive media content for annotation, identify a data model corresponding to the media content, and determine a workflow for annotating the media content based on the data model, the workflow including multiple tasks. The hardware processor further executes the model-driven annotation software code to identify one or more annotation contributors for performing the tasks included in the workflow, distribute the tasks to the one or more annotation contributors, receive inputs from the one or more contributors responsive to at least some of the tasks, and generate an annotation for the media content based on the inputs.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Abstract: According to one implementation, a system for programmatic generation of media content digests includes a computing platform having a hardware processor and a system memory storing a media content digest software code. The hardware processor executes the media content digest software code to identify a media content for use in generating a content digest, the media content including a timecode of the media content, to access a metadata describing the media content and indexed to the timecode, and to identify one or more constraints for the content digest. In addition, the hardware processor executes the media content digest software code to programmatically extract content segments from the media content using the metadata indexed to the timecode and based on the one or more constraints, and to generate the content digest based on the media content from the content segments.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: There is provided a system including a computing platform having a hardware processor and a memory, and a metadata extraction and management unit stored in the memory. The hardware processor is configured to execute the metadata extraction and management unit to extract a plurality of metadata types from a media asset sequentially and in accordance with a prioritized order of extraction based on metadata type, aggregate the plurality of metadata types to produce an aggregated metadata describing the media asset, use the aggregated metadata to include at least one database entry in a graphical database, wherein the at least one database entry describes the media asset, display a user interface for a user to view tags of metadata associated with the media asset, and correcting presence of one of the tags of metadata associated with the media asset, in response to an input from the user via the user interface.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: There is provided a system including a processor and a memory, storing an asset-data synthesis unit having a data translation module and a data integration module. The processor executes the asset-data synthesis unit to receive a first and a second domain specific asset description data being expressed using different descriptive vocabularies, translate the first and second domain specific asset description data to a common descriptive vocabulary to produce respective first and second common format asset description data, inferentially identify based on probabilistic inference that the first and second common format asset description data describe a same asset, integrate the first and second common format asset description data to produce an integrated asset description data describing the same asset, and generate a synthesized data describing the same asset and linking the integrated asset description data and the first and second domain specific asset description data.

Abstract: There are provided systems and methods for performing metadata extraction and management. Such a system includes a computing platform having a hardware processor, a system memory, and metadata extraction and management unit stored in the system memory. The system is configured to extract multiple metadata types from a media asset, and to aggregate the multiple metadata types to produce an aggregated metadata describing the media asset. The system is further configured to transform the aggregated metadata into at least one database entry identifying the media asset, and to map the at least one database entry into a graphical database so as to relate the media asset to at least one other media asset represented in the graphical database.

Abstract: There are provided asset-data integration systems and methods for use by such systems. Such a system includes a hardware processor, a system memory, and an asset-data synthesis unit including a data translation module and a data integration module, stored in the system memory. The system is configured to receive domain specific asset description data expressed using different descriptive vocabularies, and to translate the domain specific asset description data to produce common format asset description data. The system is further configured to identify the common format asset description data describing the same asset, to integrate that common format asset description data to produce an integrated asset description data describing the asset, and to generate a synthesized data describing the asset and linking the integrated asset description data and the domain specific asset description data.

Abstract: Systems, methods, and computer program products to perform an operation comprising computing a saliency value for a video based on saliency values a set of pixels in each frame of the video, computing, for the video, an expected value for a metric by a predictive algorithm based on the saliency value for the video, and outputting the expected value for the metric as an indication of an expected outcome for the metric achieved by the video.

Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

Abstract: There are provided media asset tagging systems and method. Such a system includes a hardware processor, and a system memory storing a workflow management software code including a tagging application template and a multi-contributor synthesis module. The hardware processor executes the workflow management software code to provide a workflow management interface, to receive a media asset identification data and a workflow rules data, and to generate custom tagging applications based on the workflow rules data. The hardware processor further executes the workflow management software code to receive tagging data for the media asset, determine at least a first constraint for tagging the media asset, receive additional tagging data for, and determine at least a second constraint for tagging the media asset. The media asset is then tagged based on the tagging data and the additional tagging data, subject to the constraints.

Abstract: There is provided a system including a non-transitory memory storing a media library including an ordered plurality of media contents, each including a plurality of attribute tags, and a hardware processor configured to provide a user interface for display on a user device for navigating the media contents, receive a user input from the user device for playing one or more of the ordered media contents based on a first attribute tag of the plurality of attribute tags, stream a first portion of a first media content to the user device, based on the first attribute tag of the media content selected by the user input, and stream a second portion of a second media content to the user device following the first portion of the first media content, based on the first attribute tag of the media content selected by the user input.

Abstract: There is provided a system and a method for use by the system to provide live panoramic image capture and distribution. In one implementation, such a method includes capturing multiple perspectives of a visual environment, combining at least a subset of those perspectives to produce a panoramic image of the visual environment, and encoding the panoramic image to generate panoramic image data. The method also includes transmitting the first panoramic image data to a routing and processing portal for distribution of the panoramic image in real-time.

Abstract: There are provided systems and methods for performing metadata extraction and management. Such a system includes a computing platform having a hardware processor, a system memory, and metadata extraction and management unit stored in the system memory. The system is configured to extract multiple metadata types from a media asset, and to aggregate the multiple metadata types to produce an aggregated metadata describing the media asset. The system is further configured to transform the aggregated metadata into at least one database entry identifying the media asset, and to map the at least one database entry into a graphical database so as to relate the media asset to at least one other media asset represented in the graphical database.

Abstract: Systems, methods, and computer program products to perform an operation comprising computing a saliency value for a video based on saliency values a set of pixels in each frame of the video, computing, for the video, an expected value for a metric by a predictive algorithm based on the saliency value for the video, and outputting the expected value for the metric as an indication of an expected outcome for the metric achieved by the video.

Abstract: There is provided a media content ontology system and method. The system includes a computing platform having a processor and a memory, a content genome database stored in the memory, and ontology software for execution by the processor. The ontology software is configured to map a media content asset to first and second content classifications based on respective first and second data, and to generate first and second content genome database entries associating the media content asset with other, correspondingly mapped, media content assets. In addition, the ontology software is configured to cross-index the first and second content genome database entries to enable identification of each of the first and second content classifications and the media content asset based on any one of the first or second content classifications or the media content asset.

Abstract: One embodiment provides a monopole for a camera, including: a pole of length sufficient for two handed operation; an offset arrangement attached to an end of the pole, the offset arrangement comprising a first element connected at an angle to the end of the pole and a second element connected to the first element; the first element rotating about a first axis with respect to the end of the pole; the second element rotating about a second axis with respect to the first element; a camera mount attached to the second element, wherein the camera mount rotates about a third axis with respect to the second element; and at least one motor aligned with the first, the second or the third axis and imparting movement to the camera mount with respect to the pole in at least one degree of freedom selected from the group consisting of tilt, pan and roll. Other aspects are described and claimed.

Abstract: There are provided asset-data integration systems and methods for use by such systems. Such a system includes a hardware processor, a system memory, and an asset-data synthesis unit including a data translation module and a data integration module, stored in the system memory. The system is configured to receive domain specific asset description data expressed using different descriptive vocabularies, and to translate the domain specific asset description data to produce common format asset description data. The system is further configured to identify the common format asset description data describing the same asset, to integrate that common format asset description data to produce an integrated asset description data describing the asset, and to generate a synthesized data describing the asset and linking the integrated asset description data and the domain specific asset description data.