Patents by Inventor Apostol Natsev
Apostol Natsev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20220207873Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: ApplicationFiled: December 13, 2021Publication date: June 30, 2022Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 11200423Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: GrantFiled: November 18, 2019Date of Patent: December 14, 2021Assignee: Google LLCInventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 11042553Abstract: Facilitating of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a non-transitory computer-readable medium comprises computer-readable instructions that, in response to execution, cause a computing system to perform operations. The operations include aggregating information indicative of initial entities for content and initial scores associated with the initial entities received from one or more content annotation sources and mapping the initial scores to respective values to generate calibrated scores. The operations include applying weights to the calibrated scores to generate weighted scores and combining the weighted scores using a linear aggregation model to generate a final score. The operations include determining whether to annotate the content with at least one of the initial entities based on a comparison of the final score and a defined threshold value.Type: GrantFiled: November 21, 2017Date of Patent: June 22, 2021Assignee: GOOGLE LLCInventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Weilong Yang, John Burge, Sanketh Shetty, Omid Madani
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Publication number: 20210166035Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.Type: ApplicationFiled: December 14, 2020Publication date: June 3, 2021Inventors: Sanketh Shetty, Tomas Izo, Min-Hsuan Tsai, Sudheendra Vijayanarasimhan, Apostol Natsev, Sami Abu-El-Haija, George Dan Toderici, Susana Ricco, Balakrishnan Varadarajan, Nicola Muscettola, WeiHsin Gu, Weilong Yang, Nitin Khandelwal, Phuong Le
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Patent number: 10867183Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.Type: GrantFiled: April 23, 2018Date of Patent: December 15, 2020Assignee: Google LLCInventors: Sanketh Shetty, Tomas Izo, Min-Hsuan Tsai, Sudheendra Vijayanarasimhan, Apostol Natsev, Sami Abu-El-Haija, George Dan Toderici, Susanna Ricco, Balakrishnan Varadarajan, Nicola Muscettola, WeiHsin Gu, Weilong Yang, Nitin Khandelwal, Phuong Le
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Publication number: 20200082173Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: ApplicationFiled: November 18, 2019Publication date: March 12, 2020Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 10482328Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: GrantFiled: October 2, 2017Date of Patent: November 19, 2019Assignee: Google LLCInventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 10390067Abstract: Implementations disclose predicting video start times for maximizing user engagement. A method includes receiving a first content item comprising content item segments, processing the first content item using a trained machine learning model that is trained based on interaction signals and audio-visual content features of a training set of training segments of training content items, and obtaining, based on the processing of the first content item using the trained machine learning model, one or more outputs comprising salience scores for the content item segments, the salience scores indicating which content item segment of the content item segments is to be selected as a starting point for playback of the first content item.Type: GrantFiled: May 12, 2017Date of Patent: August 20, 2019Assignee: Google LLCInventors: Sanketh Shetty, Apostol Natsev, Balakrishnan Varadarajan, Tomas Izo
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Publication number: 20180239964Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.Type: ApplicationFiled: April 23, 2018Publication date: August 23, 2018Inventors: Sanketh Shetty, Tomas Izo, Min-Hsuan Tsai, Sudheendra Vijayanarasimhan, Apostol Natsev, Sami Abu-El-Haija, George Dan Toderici, Susanna Ricco, Balakrishnan Varadarajan, Nicola Muscettola, WeiHsin Gu, Weilong Yang, Nitin Khandelwal, Phuong Le
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Patent number: 10032113Abstract: Techniques for detecting an event via social media content. A method includes obtaining multiple images from at least one social media source; extracting at least one visual semantic concept from the multiple images; differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images; retrieving one or more images associated with the event semantic concept signal; grouping the one or more images associated with the event semantic concept signal; annotating the group of one or more images with user feedback; and displaying the annotated group of one or more images as a visual description of the detected event.Type: GrantFiled: April 22, 2015Date of Patent: July 24, 2018Assignee: International Business Machines CorporationInventors: Noel C. Codella, Apostol Natsev, John R. Smith
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Patent number: 9953222Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.Type: GrantFiled: September 8, 2015Date of Patent: April 24, 2018Assignee: Google LLCInventors: Sanketh Shetty, Tomas Izo, Min-Hsuan Tsai, Sudheendra Vijayanarasimhan, Apostol Natsev, Sami Abu-El-Haija, George Dan Toderici, Susanna Ricco, Balakrishnan Varadarajan, Nicola Muscettola, WeiHsin Gu, Weilong Yang, Nitin Khandelwal, Phuong Le
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Publication number: 20180089200Abstract: Facilitating of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a non-transitory computer-readable medium comprises computer-readable instructions that, in response to execution, cause a computing system to perform operations. The operations include aggregating information indicative of initial entities for content and initial scores associated with the initial entities received from one or more content annotation sources and mapping the initial scores to respective values to generate calibrated scores. The operations include applying weights to the calibrated scores to generate weighted scores and combining the weighted scores using a linear aggregation model to generate a final score. The operations include determining whether to annotate the content with at least one of the initial entities based on a comparison of the final score and a defined threshold value.Type: ApplicationFiled: November 21, 2017Publication date: March 29, 2018Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Weilong Yang, John Burge, Sanketh Shetty, Omid Madani
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Publication number: 20180025228Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: ApplicationFiled: October 2, 2017Publication date: January 25, 2018Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 9830361Abstract: Facilitation of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a system includes an aggregation component that aggregates signals indicative of initial entities for content and initial scores associated with the initial entities generated by one or more content annotation sources; and a mapping component that maps the initial scores to calibrated scores within a defined range. The system also includes a linear aggregation component that: applies selected weights to the calibrated scores, wherein the selected weights are based on joint performance conditions; and combines the weighted, calibrated scores based on a selected linear aggregation model of a plurality of linear aggregation models to generate a final score. The system also includes an annotation component that determines whether to annotate the content with one of the initial entities based on a comparison of the final score and a defined threshold value.Type: GrantFiled: December 4, 2013Date of Patent: November 28, 2017Assignee: GOOGLE INC.Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Weilong Yang, John Burge, Sanketh Shetty, Omid Madani
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Patent number: 9779304Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: GrantFiled: August 11, 2015Date of Patent: October 3, 2017Assignee: Google Inc.Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Patent number: 9710760Abstract: A system and method for constructing a hierarchical multi-faceted classification structure includes organizing a plurality of visual categories into a multi-relational reference ontology that accounts for a plurality of different types of relationships. Media artifacts are categorized into the plurality of visual categories. The categories of artifacts are refined based on faceted ontology relationships or constraints from the multi-relational reference ontology. The multi-relational reference ontology and the one or more media artifacts with relationships are stored as the hierarchical multi-faceted classification structure in computer readable memory storage.Type: GrantFiled: June 29, 2010Date of Patent: July 18, 2017Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Matthew Hill, John R. Kender, Apostol Natsev, Quoc-Bao Nguyen, John R. Smith, Jelena Tesic, Lexing Xie, Rong Yan
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Patent number: 9659218Abstract: Implementations disclose predicting video start times for maximizing user engagement. A method includes applying a machine-learned model to audio-visual content features of segments of a target content item, the machine-learned model trained based on user interaction signals and audio-visual content features of a training set of content item segments, calculating, based on applying the machine-learned model, a salience score for each of the segments of the target content item, and selecting, based on the calculated salience scores, one of the segments of the target content item as a starting point for playback of the target content item.Type: GrantFiled: April 29, 2015Date of Patent: May 23, 2017Assignee: Google Inc.Inventors: Sanketh Shetty, Apostol Natsev, Balakrishnan Varadarajan, Tomas Izo
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Patent number: 9627004Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.Type: GrantFiled: October 14, 2015Date of Patent: April 18, 2017Assignee: Google Inc.Inventors: Balakrishnan Varadarajan, Sanketh Shetty, Apostol Natsev, Nitin Khandelwal, Weilong Yang, Sudheendra Vijayanarasimhan, WeiHsin Gu, Nicola Muscettola
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Publication number: 20170046573Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.Type: ApplicationFiled: August 11, 2015Publication date: February 16, 2017Inventors: Balakrishnan Varadarajan, George Dan Toderici, Apostol Natsev, Nitin Khandelwal, Sudheendra Vijayanarasimhan, Weilong Yang, Sanketh Shetty
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Publication number: 20160070962Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.Type: ApplicationFiled: September 8, 2015Publication date: March 10, 2016Inventors: Sanketh Shetty, Tomas Izo, Min-Hsuan Tsai, Sudheendra Vijayanarasimhan, Apostol Natsev, Sami Abu-El-Haija, George Dan Toderici, Susanna Ricco, Balakrishnan Varadarajan, Nicola Muscettola, WeiHsin Gu, Weilong Yang, Nitin Khandelwal, Phuong Le