Juwei Lu

20130279881, Oct 24, 2013

Apr 19, 2012

13/451436

Juwei Lu - Irvine CA, US
Bradley Denney - Irvine CA, US

CANON KABUSHIKI KAISHA - Tokyo

H04N 9/80

386241, 386E09011

Systems and methods for summarizing a video assign frames in a video to at least one of two or more groups based on a topic, generate a respective first similitude measurement for the frames in a group relative to the other frames in the group based on a feature, rank the frames in a group relative to one or more other frames in the group based on the respective first similitude measurement of the respective frames, and select a frame from each group as a most-representative frame based on the respective rank of the frames in a group relative to the other frames in the group.

20140015855, Jan 16, 2014

Jul 16, 2012

13/550357

Bradley Scott Denney - Irvine CA, US
Juwei Lu - Irvine CA, US
Dariusz T. Dusberger - Irvine CA, US
Sholeh Forouzan - Irvine CA, US

CANON KABUSHIKI KAISHA - Tokyo

G09G 5/00

345629

Systems and methods for clustering descriptors in a space of visual descriptors to generate augmented visual descriptors in an augmented space that includes semantic information, wherein the augmented space of the augmented descriptors includes both visual descriptor-to-descriptor dissimilarities and semantic label-to-label dissimilarities; and cluster the augmented visual descriptors in the augmented space based at least in part on a dissimilarity measure between augmented visual descriptors in the augmented descriptor space.

20140056511, Feb 27, 2014

Aug 22, 2012

13/592148

Juwei Lu - Irvine CA, US
Bradley Scott Denney - Irvine CA, US

CANON KABUSHIKI KAISHA - Tokyo

G06K 9/62

382159, 382225

Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

20140267301, Sep 18, 2014

Mar 14, 2013

13/829338

- Tokyo, JP
Bradley Scott Denney - Irvine CA, US
Juwei Lu - Irvine CA, US
Dariusz Dusberger - Irvine CA, US
Hung Khei Huang - Irvine CA, US

CANON KABUSHIKI KAISHA - Tokyo

G09G 5/24

345467

Systems and methods for generating visual words define initial inter-visual word relationships between a plurality of visual words; define visual word-image relationships between the plurality of visual words and a plurality of images; define inter-image relationships between the plurality of images; generate revised inter-visual word relationships in a vector space based on the initial inter-visual word relationships, the inter-image relationships, and the visual word-image relationships; and generate higher-level visual words in the vector space based on the revised inter-visual word relationships.

20140272822, Sep 18, 2014

Mar 14, 2013

13/830247

- Tokyo, JP
Bradley Scott Denney - Irvine CA, US
Juwei Lu - Irvine CA, US
Dariusz Dusberger - Irvine CA, US
Hung Khei Huang - Irvine CA, US

CANON KABUSHIKI KAISHA - Tokyo

G09B 5/02

434167

Systems and methods for learning a high-level visual vocabulary generate inter-visual-word relationships between a plurality of visual words based on visual word-label relationships, map the visual words to a vector space based on the inter-visual word relationships, and generate high-level visual words in the vector space.

20140093174, Apr 3, 2014

Sep 28, 2012

13/629948

- Tokyo, JP
Bradley Scott Denney - Irvine CA, US
Juwei Lu - Irvine CA, US

G06K 9/46

382190

Systems and methods for organizing images extract low-level features from an image of a collection of images of a specified event, wherein the low-level features include visual characteristics calculated from the image pixel data, and wherein the specified event includes two or more sub-events; extract a high-level feature from the image, wherein the high-level feature includes characteristics calculated at least in part from one or more of the low-level features; identify a sub-events in the image based on the high-level feature and a predetermined model of the specified event, wherein the predetermined model describes a relationship between two or more sub-events; and annotate the image based on the identified sub-event.