| Patent application number | Description | Published |
|---|
| 20090006368 | Automatic Video Recommendation - Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking. | 01-01-2009 |
| 20090079871 | ADVERTISEMENT INSERTION POINTS DETECTION FOR ONLINE VIDEO ADVERTISING - Systems and methods for determining insertion points in a first video stream are described. The insertions points being configured for inserting at least one second video into the first video. In accordance with one embodiment, a method for determining the insertion points includes parsing the first video into a plurality of shots. The plurality of shots includes one or more shot boundaries. The method then determines one or more insertion points by balancing a discontinuity metric and an attractiveness metric of each shot boundary. | 03-26-2009 |
| 20090083781 | Intelligent Video Player - Systems and methods for managing digital video data are described. The digital video data maybe managed by employing a computing device to extract metadata from the video file and calculate a unique video signature associated with the video file. The computing device then uploads the metadata and unique video signature to a server which stores the metadata in a lookup table according to the unique video signature. | 03-26-2009 |
| 20100082614 | BAYESIAN VIDEO SEARCH RERANKING - A general framework for video search reranking is disclosed which explicitly formulates reranking into a global optimization problem from the Bayesian perspective. Under this framework, with two novel pair-wise ranking distances, two effective video search reranking methods, hinge reranking and preference strength reranking, are disclosed. Experiments conducted on the TRECVID dataset have demonstrated that the disclosed methods outperform several existing reranking approaches. | 04-01-2010 |
| 20100106671 | Comprehensive Human Computation Framework - Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers. | 04-29-2010 |
| 20100153219 | IN-TEXT EMBEDDED ADVERTISING - Computer program products, devices, and methods for generating in-text embedded advertising are described. Embedded advertising is “hidden” or embedded into a message by matching an advertisement to the message and identifying a place in the message to insert the advertisement. For textual messages, statistical analysis of individual sentences is performed to determine where it would be most natural to insert an advertisement. Statistical rules of grammar derived from a language model may be used choose a natural and grammatical place in the sentence for inserting the advertisement. Insertion of the advertisement creates a modified sentence without degrading a meaning of the original sentence, yet also includes the advertisement as a part of a new sentence. | 06-17-2010 |
| 20100185624 | COLORBLIND ACCESSIBLE IMAGE SEARCH - Colorblind accessible image search technique embodiments are presented that re-rank the results of a relevance-ranked image search to account for the accessibility of the images to a colorblind person. This is accomplished by first computing a colorblind accessibility quantity for each image of interest in the search results. A colorblind accessibility quantity quantizes the degree to which color information is preserved when an image is perceived by a colorblind person viewing the image. It is computed by generating a colorblind version of an image that simulates how the image would appear to the colorblind person. An amount quantifying the loss of color information between the image and the colorblind version of the image is then estimated. This estimate is used to compute the colorblind accessibility quantity for the image. Once the colorblind accessibility quantities have been computed, the image search results are re-ranked based on these quantities. | 07-22-2010 |
| 20100205202 | Visual and Textual Query Suggestion - Techniques described herein enable better understanding of the intent of a user that submits a particular search query. These techniques receive a search request for images associated with a particular query. In response, the techniques determine images that are associated with the query, as well as other keywords that are associated with these images. The techniques then cluster, for each set of images associated with one of these keywords, the set of images into multiple groups. The techniques then rank the images and determine a representative image of each cluster. Finally, the tools suggest, to the user that submitted the query, to refine the search based on user selection of a keyword and a representative image. Thus, the techniques better understand the user's intent by allowing the user to refine the search based on another keyword and based on an image on which the user wishes to focus the search. | 08-12-2010 |
| 20100217732 | Unbiased Active Learning - Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements. | 08-26-2010 |
| 20100228691 | Media Tag Recommendation Technologies - Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation. | 09-09-2010 |
| 20100250190 | TAG RANKING - Technologies for generating a boosted tag ranking for a media instance, the boosted tag ranking based on probabilistic relevance estimation and tag correlation refining. Such boosted tag rankings may be used for search result ranking, tag recommendation, and group recommendation. | 09-30-2010 |
