Microsoft Technology Licensing, LLC

National University of Singapore

Meng Wang

Xian-Sheng Hua

Hong-Jiang Zhang

Tao Mei

Linjun Yang

Kuiyuan Yang

Guangda Li

Zhengjun Zha

Tat Seng Chua

Zheng Lu

Yelong Sheng

Yan Song

A system provides product annotation in a video to one or more users. The system receives a video from a user, where the video includes multiple video frames. The system extracts multiple key frames from the video and generates a visual representation of the key frame. The system compares the visual representation of the key frame with a plurality of product visual signatures, where each visual signature identifies a product. Based on the comparison of the visual representation of the key frame and a product visual signature, the system determines whether the key frame contains the product identified by the visual signature of the product. To generate the plurality of product visual signatures, the system collects multiple training images comprising multiple of expert product images obtained from an expert product repository, each of which is associated with multiple product images obtained from multiple web resources.

In-video product annotation with web information mining

A computing device configured to determine that one or more regions of an image are associated with a tag of the image is described herein. The computing device is further configured to determine one or more attribute tags describing at least one of the content or context of the one or more regions. Upon determining the attribute tags, the computing device associates the attribute tags with the tag to enable image searching based on the tag and attribute tags.

Tag association with image regions

Methods and systems for in-image accessibility indication are described herein. These methods and systems are usable to receive an image being designed, locate colorblind inaccessible regions of the image based on located colorblind inaccessible points, and indicate the located colorblind inaccessible regions in the image to a designer while the designer is creating the image. As such, the designer is able to quickly identify regions of the designed image that may be difficult for colorblind viewers to see and, with this information, the designer is able to modify the image accordingly.

In-image accessibility indication

Methods and systems for object-sensitive image searches are described herein. These methods and systems are usable for receiving a query for an image of an object and providing a ranked list of query results to the user based on a ranking of the images. The object-sensitive image searches may generate a pre-trained multi-instance learning (MIL) model trained from free training data from users sharing images at websites to identify a common pattern of the object, and/or may generate a MIL model 'on the fly' trained from pseudo-positive and pseudo-negative samples of query results to identify a common pattern of the object. As such, the user is presented with query results that include images that prominently display the object near the top of the results.

Object-sensitive image search

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.

Growing community of inventors

Singapore, Singapore

Meng Wang