Microsoft Technology Licensing, LLC

Google Inc.

Bingbing Ni

Yang Song

Ming Zhao

Linjun Yang

Qi Tian

Adaptive image retrieval image allows retrieval of images that are more likely to reflect a current trend of user preferences and/or interests, and therefore can provide relevant results to an image search. Adaptive image retrieval includes receiving image query log data from one or more clients, and updating a codebook of features based on the received query log data. The image query log data includes images that have been queried by the one or more clients within a predetermined period of time.

Adaptive image retrieval database

Systems and methods are provided herein relating to video classification. A text mining component is disclosed that automatically generates a plurality of video event categories. Part-of-Speech (POS) analysis can be applied to video titles and descriptions, further using a lexical hierarchy to filter potential classifications. Classification performance can be further improved by extracting content-based features from a video sample. Using the content based features a set of classifier scores can be generated. A hyper classifier can use both the classifier scores and the content-based features of the video to classify the video sample.

Large scale video event classification

A hierarchical sparse codebook allows efficient search and comparison of images in image retrieval. The hierarchical sparse codebook includes multiple levels and allows a gradual determination/classification of an image feature of an image into one or more groups or nodes by traversing the image feature through one or more paths to the one or more groups or nodes of the codebook. The image feature is compared with a subset of nodes at each level of the codebook, thereby reducing processing time.

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Singapore, Singapore

Bingbing Ni