Microsoft Technology Licensing, LLC

Saurajit Mukherjee

Arun Sacheti

Kuang-Huei Lee

Yan Wang

Li Huang

Meenaz Merchant

Surendra Ulabala

Nikesh Srivastava

Fnu Yokesh Kumar

Linjun Yang

Houdong Hu

Kamal Ginotra

Yokesh Kumar

Kun Wu

Tianjun Xiao

Andrew Lawrence Stewart

Hanmu Zuo

Zhiting Chen

Rui Xia

Chu Wang

Systems and methods directed to returning personalized image-based search results are described. In examples, a query including an image may be received, and a personalized item embedding may be generated based on the image and user profile information associated with a user. Further, a plurality of candidate images may be obtained based on the personalized item embedding. The candidate images may then be ranked according to a predicted level of user engagement for a user, and then diversified to ensure visual diversity among the ranked images. A portion of the diversified images may then be returned in response to an image-based search.

Web-scale personalized visual search recommendation service

The description relates to diversified hybrid image annotation for annotating images. One implementation includes generating first image annotations for a query image using a retrieval-based image annotation technique. Second image annotations can be generated for the query image using a model-based image annotation technique. The first and second image annotations can be integrated to generate a diversified hybrid image annotation result for the query image.

Image annotation

Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.

Ensemble model for image recognition processing

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Kirkland, WA, United States of America

Saurajit Mukherjee