Microsoft Technology Licensing, LLC

Haim Sabo

Avner Levi

Irit Ofer

Lei Zhang

Oron Nir

Ika Bar-Menachem

Eylon Ami

Tucker Cunningham Burns

Ella Ben Tov

Apar Singhal

Maria Zontak

Yonit Hoffman

Anika Zaman

Reut Amior

A system for indexing animated content receives detections extracted from a media file, where each one of the detections includes an image extracted from a corresponding frame of the media file that corresponds to a detected instance of an animated character. The system determines, for each of the received detections, an embedding defining a set of characteristics for the detected instance. The embedding associated with each detection is provided to a grouping engine that is configured to dynamically configure at least one grouping parameter based on a total number of the detections received. The grouping engine is also configured to sort the detections into groups using the grouping parameter and the embedding for each detection. A character ID is assigned to each one of the groups of detections, and the system indexes the groups of detections in a database in association with the character ID assigned to each group.

Dynamic detection and recognition of media subjects

The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Negative sampling algorithm for enhanced image classification

The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Semi supervised target recognition in video

Semi supervised animated character recognition in video

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Petah Tikva, Israel

Haim Sabo