Microsoft Technology Licensing, LLC

Xin-Jing Wang

Lei Zhang

Wei-Ying Ma

Motaz Ahmed El-Saban

May Abdelreheem Sayed

Xi Chen

Ming Gang Liu

Juan Xu

Rui Cai

Matthew Robert Scott

Rui Hu

Yi Li

Jiang-Ming Yang

Xiao Kong

Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content including the clusters may be presented to provide an enhanced user experience.

Organizing search results based upon clustered content

A candidate for selection or insertion may be based on extracted candidate features and reference features. The candidate features may be scored and ranked while candidates may be removed from consideration based on criteria. The remaining candidates may be presented for selection and/or insertion.

Feature-based candidate selection

Techniques for identifying near-duplicates of a media object and associating metadata of the near-duplicates with the media object are described herein. One or more devices implementing the techniques are configured to identify the near duplicates based at least on similarity attributes included in the media object. Metadata is then extracted from the near-duplicates and is associated with the media object as descriptors of the media object to enable discovery of the media object based on the descriptors.

Associating media with metadata of near-duplicates

Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content based upon the clusters may be presented to provide an enhanced user experience.

Techniques are described for online real time text to image translation suitable for virtually any submitted query. Semantic classes and associated analogous items for each of the semantic classes are determined for the submitted query. One or more requests are formulated that are associated with analogous items. The requests are used to obtain web based images and associated surrounding text. The web based images are used to obtain associated near-duplicate images. The surrounding text of images is analyzed to create high-quality text associated with each semantic class of the submitted query. One or more query dependent classifiers are trained online in real time to remove noisy images. A scoring function is used to score the images. The images with the highest score are returned as a query response.

Text to image translation

An annotation suggestion platform may comprise a client and a server, where the client captures a media object and sends the captured object to the server, and the server provides a list of suggested annotations for a user to associate with the captured media object. The user may then select which of the suggested metadata is to be associated or stored with the captured media. In this way, a user may more easily associate metadata with a media object, facilitating the media object's search and retrieval. The server may also provide web page links related to the captured media object. Suggested metadata may be viewed, selected, and associated through a user interface. The user interface may additionally provide space for web pages to be navigated and viewed.

Real-time annotation and enrichment of captured video

Implementations for identifying duplicate images in an image space are described. An image space is partitioned into a plurality of coarse clusters based on signatures of the images within the image space. The signatures are determined from compact descriptors of the images. Refined clusters that include one or more images of an individual coarse cluster are created based on pair-wise comparisons of the compact descriptors of images in the coarse cluster, and the refined clusters are identified as sets of duplicate images. The refined clusters are grown by searching in similar coarse clusters for images to add to the refined clusters.

Identification of duplicates within an image space

An annotation suggestion platform may comprise a client and a server, where the client captures a media object and sends the captured object to the server, and the server provides a list of suggested annotations for a user to associate with the captured media object. The user may then select which of the suggested metadata is to be associated or stored with the captured media. In this way, a user may more easily associate metadata with a media object, facilitating the media object's search and retrieval. The server may also provide web page links related to the captured media object. A user interface for the annotation suggestion platform is also described herein, as are optimizations including indexing and tag propagation.

While browsing, a user may interact with a wide variety of images. The user may upload and share images taken with a digital camera and/or search for image using a search engine. Because images are rich in contextual information, it may be advantageous to provide additional information, such as adjacent market advertising based upon matching advertisements with contextual information of the images. Accordingly, a query image may be used to retrieve a video frame set. The video frame set may be expanded with related video frames corresponding to adjacent markets. The expanded video frame set may be grouped into clusters of similar frames. The clusters may be used to rank advertisements based upon how similar the advertisements are to the clusters and/or video frames within the clusters. In this way, one or more ranked advertisements may be presented with the query image.

Ranking advertisement(s) based upon advertisement feature(s)

Search queries for images are received from users. An original order of responsive images to the query is determined. Duplicate images and words associated with the duplicate images are identified for each of the responsive images. Common words associated with the duplicate images are identified. The responsive images are annotated with the common words and an annotated order is determined. A re-ranked order is determined based on the original order and the annotated order. Responsive images are presented to the user in the re-ranked order.

Image re-rank based on image annotations

Names of entities, such as people, in an image may be identified automatically. Visually similar images of entities are retrieved, including text proximate to the visually similar images. The collected text is mined for names of entities, and the detected names are analyzed. A name may be associated with the entity in the image, based on the analysis.

Building a person profile database

In some implementations, a plurality of first questions and corresponding first answers are identified at a community question-answer (CQA) site as a plurality of first question-answer (q-a) pairs. A query thread comprised of a second question and a plurality of candidate second answers is selected for making a determination of answer quality. A set of the first questions that are similar to the second question are identified from the plurality of first questions. First linking features between the identified set of first questions and their corresponding first answers are used for determining an analogy with second linking features between the second question and candidate answers for ranking the candidate answers.

Answer ranking in community question-answering sites

A simultaneous semantic and structure threaded discussion modeling system and method for generating a model of a discussion thread and using the model to mine data from the discussion thread. Embodiments of the system and method generate a model that contains both semantic terms and structure terms. The model simultaneously models both semantics and structure of the discussion thread. A model generator includes a semantic module generates two semantic terms for the model and a structure module generates two structure terms for the model. The generator combines the two semantic terms and the two structure terms to generate the simultaneous semantic and structure model. Embodiments of the system and method include an applications module, which contains three application that use the model to reconstruct reply relations among posts in the discussion thread, identify junk posts in the discussion thread, and find experts in each sub-board of web forums.

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Beijing, China

Xin-Jing Wang