Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: A method of providing in-context exploration of a query results web page, wherein the web page has at least one element and a plurality of query results, receives an indication of interest in the at least one element, and in response to the indication, emphasizes at least one query result relative to the plurality of query results.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented using one or more computing devices to facilitate or otherwise support one or more processes or operations associated with ranking entity facets using user-click feedback.

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.

Abstract: Briefly, embodiments of a method or system of predicting media content preferences are disclosed.

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.

Abstract: Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model.

Abstract: Subject matter disclosed herein relates to electronic image object or logo recognition.

Abstract: A variety of computer based service that permit users to edit, compose, upload, or otherwise generate content also provide for the integration of sponsored media into presentations along with user-generated content. An exemplary service generates text based on user input, provides tags based on the text to a sponsored media repository, receives a sponsored media data structure in return, and formats sponsored media from the data structure for display to the user.

Abstract: A variety of computer based service that permit users to edit, compose, upload, or otherwise generate content also provide for the integration of sponsored media into presentations along with user-generated content. An exemplary service generates text based on user input, provides tags based on the text to a sponsored media repository, receives a sponsored media data structure in return, and formats sponsored media from the data structure for display to the user.

Abstract: Subject matter disclosed herein relates to electronic image object or logo recognition.

Abstract: Multimedia features extracted from display advertisements may be integrated into a click prediction model for improving click prediction accuracy. Multimedia features may help capture the attractiveness of ads with similar contents or aesthetics. Numerous multimedia features (in addition to user, advertiser and publisher features) may be utilized for the purposes of improving click prediction in ads with limited or no history.

Abstract: An approach for responding to a text-based query for a digital image is provided. A request that identifies one or more keywords is received. A number of annotated digital images are selected based on a previously determined optimum quantity of annotated digital images. Composite data is gathered from each annotated digital image and a set of candidate digital images is selected based on the composite data. The set of candidate images are the digital images, of a set of digital images, which have a visual appearance that is most similar to the composite data. A response is generated that identifies those digital images which are most responsive to the one or more keywords. Alternatively, a partitioned response is generated which identifies dissimilar sets of digital images.

Abstract: Subject matter disclosed herein relates to image object recognition or retrieval.

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model.

Abstract: Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.

Abstract: The present invention is directed towards systems and methods for extending media annotations using collective knowledge. The method according to one embodiment of the present invention comprises receiving a plurality of content items and associated annotations. The method further normalizes the plurality of associated annotations and calculates pair frequencies for the plurality of associated annotations. The method then retrieves a plurality of alternative annotations and provides the plurality of alternative annotations.

Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: Disclosed herein is an ability to create segments of content, such as video, audio, multimedia, image, animation, interactive, etc. content, to associate metadata to each segment created, and to generate a summary of content comprising one or more segments using the metadata associated with the content segments. One or more acticons represented as icons in a user interface displayable to a user can be used to indicate a starting and/or ending point for a content segment. An acticon can represent an occurrence in the content, or a reaction of a user experiencing the content, for example.