Patent application number | Description | Published |
20120030152 | RANKING ENTITY FACETS USING USER-CLICK FEEDBACK - 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. | 02-02-2012 |
20120084159 | USER CREDIBILITY IN ELECTRONIC MEDIA ADVERTISING - A method or system for employing user credibility in electronic media advertising is disclosed. | 04-05-2012 |
20120084226 | MEASURING OR ESTIMATING USER CREDIBILITY - A method or system for measuring or estimating user credibility is described. | 04-05-2012 |
20120084302 | MEDIA OR CONTENT TAGGING DETERMINED BY USER CREDIBILITY SIGNALS - Briefly, one or more embodiments of methods, apparatuses or systems for media or content tagging are described. | 04-05-2012 |
20120158716 | IMAGE OBJECT RETRIEVAL BASED ON AGGREGATION OF VISUAL ANNOTATIONS - 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. | 06-21-2012 |
20120263385 | LOGO OR IMAGE RECOGNITION - Subject matter disclosed herein relates to electronic image object or logo recognition. | 10-18-2012 |
20130097285 | MEDIA ENRICHMENT SYSTEM AND METHOD - 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. | 04-18-2013 |
20130142418 | RANKING AND SELECTING REPRESENTATIVE VIDEO IMAGES - 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. | 06-06-2013 |
20130148880 | Image Cropping Using Supervised Learning - 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. | 06-13-2013 |
20130346182 | MULTIMEDIA FEATURES FOR CLICK PREDICTION OF NEW ADVERTISEMENTS - 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. | 12-26-2013 |