Abstract

A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

IPC Classes  ?

• G06F 16/45 - ClusteringClassification
• G06F 16/483 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 20/00 - Machine learning
• G06V 20/40 - ScenesScene-specific elements in video content

Abstract

A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

IPC Classes  ?

• G06F 16/45 - ClusteringClassification
• G06F 16/483 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 20/00 - Machine learning
• G06V 20/40 - ScenesScene-specific elements in video content

Abstract

The present invention includes a system for improving the visual quality of recorded videos, especially screen recordings such as webinars. The system automatically detects the boundaries of individual tile elements on a screen recording, and then performs facial recognition in order to identify which tiles include a human face and for tracking that face even when the tiles are repositioned over the course of the video. The system uses liveness detection to determine which tiles are video tiles and which tiles are screenshare tiles and then automatically shifts the relative position of the video tiles and the screenshare tiles to create an improved aesthetic quality of the recorded videos. The system is further capable of automatically integrating a company's color branding or logos into the recorded videos.

IPC Classes  ?

• G11B 27/036 - Insert-editing
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 20/40 - ScenesScene-specific elements in video content
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

IPC Classes  ?

• G06V 20/40 - ScenesScene-specific elements in video content
• G06F 16/45 - ClusteringClassification
• G06F 16/483 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 20/00 - Machine learning

Abstract

The present invention includes a system for improving the visual quality of recorded videos, especially screen recordings such as webinars. The system automatically detects the boundaries of individual tile elements on a screen recording, and then performs facial recognition in order to identify which tiles include a human face and for tracking that face even when the tiles are repositioned over the course of the video. The system uses liveness detection to determine which tiles are video tiles and which tiles are screenshare tiles and then automatically shifts the relative position of the video tiles and the screenshare tiles to create an improved aesthetic quality of the recorded videos. The system is further capable of automatically integrating a company's color branding or logos into the recorded videos.

IPC Classes  ?

• G11B 27/036 - Insert-editing
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 20/40 - ScenesScene-specific elements in video content
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

IPC Classes  ?

• G06V 20/40 - ScenesScene-specific elements in video content
• G06F 16/45 - ClusteringClassification
• G06F 16/483 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 20/00 - Machine learning

Abstract

A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

IPC Classes  ?

• G06V 20/40 - ScenesScene-specific elements in video content
• G06F 16/45 - ClusteringClassification
• G06F 16/483 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 20/00 - Machine learning