Gracenote, Inc.

Konstantinos Antonio Dimitriou

Amanmeet Garg

Aneesh Vartakavi

Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.

Automated video segmentation

Methods and systems for disambiguating musical artist names are disclosed. Musical-artist-release records (MARRs) may be input to a multi-modal artificial neural network (ANN). Each MARR may be associated with a musical release of an artist, and may include a release ID and an artist ID, and release data in categories including music media content and metadata categories including sub-definitive musician name of the artist and release subcategories. All n-tuples of MARRs may be formed, and for each n-tuple, the ANN may be applied concurrently to each MARR to generate a release feature vector (RFV) that includes a set of sub-feature vectors, each characterizing a different category of release data. For each n-tuple, the ANN may be trained to cluster in a multi-dimensional RFV space RFVs of the same artist ID, and to separate RFVs of different artist IDs. The MARRs and their RFVs may be stored in a release database.

Music release disambiguation using multi-modal neural networks

Systems and methods for monitoring of icon in an external display device are disclosed. Images of an icon displayed in a display device may be continually captured as video frames by a video camera of an icon monitoring system. While operating in a first mode, video frames may be continually analyzed to determine if the captured image matches an active template icon known to match the captured image of the icon. While the captured image matches the active template icon, operating in the first mode continues. Upon detecting a failed match to the active template icon, the system starts operating in a second to search among known template icons for a new match. Upon finding a new match, the active template icon may be updated to the new match, and operation switches back to the first mode. Times of transitions between the first and second modes may be recorded.

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Berkeley, CA, United States of America

Konstantinos Antonio Dimitriou