Abstract: Social network postings, including text, images or other media, may provide valuable information regarding a user of the social network with which the postings may be associated. With the authorization of the user, and upon authentication by the social network, an online marketplace may access the social network postings and extract data therefrom, and market one or more recommended items to the user based on the extracted data, which may include color pallets or texture pallets derived from photographs included in the postings.

Abstract: A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Abstract: Techniques are described for providing functionality to allow a viewer of a television show to watch a “previously on” segment of an episode of the television show and be able to watch the scenes from prior episodes referenced in the “previously on” segment.

Abstract: Social network postings, including text, images or other media, may provide valuable information regarding a user of the social network with which the postings may be associated. With the authorization of the user, and upon authentication by the social network, an online marketplace may access the social network postings and extract data therefrom, and market one or more recommended items to the user based on the extracted data, which may include color pallets or texture pallets derived from photographs included in the postings.

Abstract: In one embodiment, a method includes by one or more computing machines: receiving a search query from a user, generating a normalized query based on the search query, identifying multiple objects matching the search query, and calculating an engagement score for each object. The engagement score is based on real-time counters and batch counters. Each counter includes: a key listing the object, normalized query, and one of multiple types of user interactions; and a value indicating a number of user interactions with the object performed in response to search queries normalizing to the normalized query. The value for real-time and batch counters indicates user interactions during first and second threshold windows of time, respectively. The method continues with sending, to the user, a search-results interface including results corresponding to the identified objects above a threshold engagement score, and updating the real-time counters based on user interactions with the search results.

Abstract: Disclosed are various embodiments for relating items to objects detected in media. An item recognition application obtains media information associated with a broadcast being consumed form the client device. The item recognition application identifies objects in the media based on user input of the location of the object on the client device display and/or heuristic detection of the object. The user may indicate the object's location through an input device. The object may be heuristically detected based on an algorithm that differentiates the object from the background. The item recognition application identifies items related to the object. The item recognition application then performs an action with the item.

Abstract: A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Abstract: Techniques are described for providing functionality to allow a viewer of a television show to watch a “previously on” segment of an episode of the television show and be able to watch the scenes from prior episodes referenced in the “previously on” segment.

Abstract: A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Abstract: A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Abstract: Automatic replay or skip ahead functionality can be configured to intelligently navigate to a portion of a video a user desires to view. The context at which a user selects intelligent navigation can be analyzed to determine where to initiate automatic replay or skip ahead. The context for intelligent navigation can be based on scene or shot segmentation data, closed captioning, aggregate video navigation data from a community of users of shared demographic traits and/or interest, and/or other metadata. In the case of automatic replay, playback of a portion of a video can include enhancements for that portion, such as providing closed captioning, display at a decreased frame rate (“slow motion”), zooming in/out on a portion of the frames of a video segment, among other enhancements.

Abstract: Automatic replay or skip ahead functionality can be configured to intelligently navigate to a portion of a video a user desires to view. The context at which a user selects intelligent navigation can be analyzed to determine where to initiate automatic replay or skip ahead. The context for intelligent navigation can be based on scene or shot segmentation data, closed captioning, aggregate video navigation data from a community of users of shared demographic traits and/or interest, and/or other metadata. In the case of automatic replay, playback of a portion of a video can include enhancements for that portion, such as providing closed captioning, display at a decreased frame rate (“slow motion”), zooming in/out on a portion of the frames of a video segment, among other enhancements.

Abstract: A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.