Abstract: An automated content recognition system accurately and reliably generates content identification information for multimedia content without accessing the multimedia content or a reliable source of the multimedia content. The system receives content-based queries having fingerprints of multimedia content. The system compares the individual queries to one another to match queries and thereby form query clusters that correspond to the same multimedia content. The system aggregates identification information from the queries in a cluster to generate reliable content identification information from otherwise unreliable identification information.

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

Abstract: Technology described herein provides techniques for screen detection. This could be a dynamic screen, such as a display screen of an electronic device, or a static screen such as a street sign. In one aspect, a screen is detected in frames of image data. An active image and/or an average color image may be formed based on frames of image data. A set of lines that are candidate for boundaries of a screen are identified in the frames. The active image and/or an average color is integrated on both sides of each line in the set of lines to form integration results. A set of screen candidates are formed based on the integration results. Screens in the screen candidates are scored according to a criterion. One or top few of the screens in the set of screen candidates is selected based on the scoring.

Abstract: Systems and methods for identifying and searching video are disclosed. A video search and identification system includes a catalog representing relationships between video, data and/or objects to enable querying and search based on visual representations of video as well as data or other information associated with the video. In one example, the catalog includes video nodes, metadata nodes and card nodes, although additional or fewer node types may be used. A visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. Confidence measures are generated at the bit level to assist in query signature processing.

Abstract: A computer-implemented system and method for monitoring the electronic communications of a subject to determine if dangerous behavior is occurring, especially in social networking platforms. The PG Guard™ web service permits a parent to monitor all of a child's activities on a social site such as Facebook®. To maintain the privacy of the child, the service only provides information to the parents about suspicious activities comprising, for example: conversations regarding violence, sex, alcohol, etc.; when a stranger interacts with their child (or vice versa); when their child discloses personal information; when a friend seems to be of a problematic nature; when a child uploads or is tagged in new images; and when a child adds or rejects a new friend. The method comprises generating a profile of the subject based on their keywords and the probability of their intended meaning, as well as their posted “Likes” and “Recommendations”.

Abstract: Systems and methods for identifying and searching video are disclosed. A video search and identification system includes a catalog representing relationships between video, data and/or objects to enable querying and search based on visual representations of video as well as data or other information associated with the video. In one example, the catalog includes video nodes, metadata nodes and card nodes, although additional or fewer node types may be used. A visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. Confidence measures are generated at the bit level to assist in query signature processing.

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

Abstract: A video visual and audio query system for quickly identifying video within a large known corpus of videos being played on any screen or display. In one embodiment, the system can record via a mobile phone camera and microphone a live video clip from the TV and transcode it into a sequence of frame-signatures. The signatures representative of the clips can then be matched against the signatures of the TV content in a corpus across a network to identify the correct TV show or movie.

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

Abstract: A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).