The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 29, 2019

Filed:

Jun. 30, 2016
Applicants:

Samsung Electrônica Da Amazônia Ltda., Campinas, BR;

Universidade Estadual DE Campinas, Campinas, BR;

Inventors:

Sandra Avila, Campinas, BR;

Daniel Moreira, Campinas, BR;

Mauricio Perez, Campinas, BR;

Daniel Moraes, Campinas, BR;

Vanessa Testoni, Campinas, BR;

Siome Goldenstein, Campinas, BR;

Eduardo Valle, Campinas, BR;

Anderson Rocha, Campinas, BR;

Assignees:

SAMSUNG ELETRÔNICA DA AMACÔNIA LTDA., Campinas, Säo Paulo, BR;

UNIVERSIDADE ESTADUAL DE CAMPINAS, Campinas, Säo Paulo, BR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); H04N 21/454 (2011.01); G06F 17/30 (2006.01); H04L 29/06 (2006.01); H04N 21/4545 (2011.01); G06K 9/46 (2006.01); G10L 25/57 (2013.01); G06K 9/00 (2006.01);
U.S. Cl.
CPC ...
H04N 21/4542 (2013.01); G06F 17/30817 (2013.01); G06K 9/00718 (2013.01); G06K 9/00765 (2013.01); G06K 9/4676 (2013.01); G06K 9/6269 (2013.01); G10L 25/57 (2013.01); H04L 65/60 (2013.01); H04N 21/4545 (2013.01); G06K 9/629 (2013.01); G06K 9/6232 (2013.01); G06K 9/6293 (2013.01);
Abstract

A multimodal and real-time method for filtering sensitive content, receiving as input a digital video stream, the method including segmenting digital video into video fragments along the video timeline; extracting features containing significant information from the digital video input on sensitive media; reducing the semantic difference between each of the low-level video features, and the high-level sensitive concept; classifying the video fragments, generating a high-level label (positive or negative), with a confidence score for each fragment representation; performing high-level fusion to properly match the possible high-level labels and confidence scores for each fragment; and predicting the sensitive time by combining the labels of the fragments along the video timeline, indicating the moments when the content becomes sensitive.


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