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公开(公告)号：US11222247B2
公开(公告)日：2022-01-11
申请号：US16999616
申请日：2020-08-21
Applicant: Dropbox, Inc.
Inventor： Thomas Berg , Peter Neil Belhumeur
Abstract: Computer-implemented techniques for sematic image retrieval. According to one technique, digital images are classified into N number of categories based on their visual content. The classification provides a set of N-dimensional image vectors for the digital images. Each image vector contains up to N number of probability values for up to N number of corresponding categories. An N-dimensional image match vector is generated that projects an input keyword query into the vector space of the set of image vectors by computing the vector similarities between a word vector for the input query and a word vector for each of the N number of categories. Vector similarities between the image match vectors and the set of image vectors can be computed to determine images semantically relevant to the input query.
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公开(公告)号：US20200380320A1
公开(公告)日：2020-12-03
申请号：US16999616
申请日：2020-08-21
Applicant: Dropbox, Inc.
Inventor： Thomas Berg , Peter Neil Belhumeur
Abstract: Computer-implemented techniques for sematic image retrieval. According to one technique, digital images are classified into N number of categories based on their visual content. The classification provides a set of N-dimensional image vectors for the digital images. Each image vector contains up to N number of probability values for up to N number of corresponding categories. An N-dimensional image match vector is generated that projects an input keyword query into the vector space of the set of image vectors by computing the vector similarities between a word vector for the input query and a word vector for each of the N number of categories. Vector similarities between the image match vectors and the set of image vectors can be computed to determine images semantically relevant to the input query.
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公开(公告)号：US10769502B1
公开(公告)日：2020-09-08
申请号：US16378261
申请日：2019-04-08
Applicant: Dropbox, Inc.
Inventor： Thomas Berg , Peter Neil Belhumeur
Abstract: Computer-implemented techniques for sematic image retrieval are disclosed. Digital images are classified into N number of categories based on their visual content. The classification provides a set of N-dimensional image vectors for the digital images. Each image vector contains up to N number of probability values for up to N number of corresponding categories. An N-dimensional image match vector is generated that projects an input keyword query into the vector space of the set of image vectors by computing the vector similarities between a word vector for the input query and a word vector for each of the N number of categories. Vector similarities between the image match vectors and the set of image vectors can be computed to determine images semantically relevant to the input query.
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