Discriminative regions: A substrate for analyzing life-logging image sequences

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Standard

Discriminative regions : A substrate for analyzing life-logging image sequences. / Moghimi, Mohammad; Kerr, Jacqueline; Johnson, Eileen; Godbole, Suneeta; Belongie, Serge.

I: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, s. 357-368.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Moghimi, M, Kerr, J, Johnson, E, Godbole, S & Belongie, S 2015, 'Discriminative regions: A substrate for analyzing life-logging image sequences', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), s. 357-368. https://doi.org/10.1007/978-3-319-14442-9_42

APA

Moghimi, M., Kerr, J., Johnson, E., Godbole, S., & Belongie, S. (2015). Discriminative regions: A substrate for analyzing life-logging image sequences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 357-368. https://doi.org/10.1007/978-3-319-14442-9_42

Vancouver

Moghimi M, Kerr J, Johnson E, Godbole S, Belongie S. Discriminative regions: A substrate for analyzing life-logging image sequences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015;357-368. https://doi.org/10.1007/978-3-319-14442-9_42

Author

Moghimi, Mohammad ; Kerr, Jacqueline ; Johnson, Eileen ; Godbole, Suneeta ; Belongie, Serge. / Discriminative regions : A substrate for analyzing life-logging image sequences. I: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015 ; s. 357-368.

Bibtex

@inproceedings{4c58ddf3edf143ee87066059384b4b1e,
title = "Discriminative regions: A substrate for analyzing life-logging image sequences",
abstract = "Life-logging devices are becoming ubiquitous, yet still processing and extracting information from the vast amount of data that is being captured is a very challenging task. We propose a method to find discriminative regions which we define as regions that are salient, consistent, repetitive and discriminative. We explain our fast and novel algorithm to discover the discriminative regions and show different applications for discriminative regions such as summarization, classification and image search. Our experiments show that our algorithm is able to find discriminative regions and discriminative patches in a short time and extracts great results on our life-logging SenseCam dataset.",
author = "Mohammad Moghimi and Jacqueline Kerr and Eileen Johnson and Suneeta Godbole and Serge Belongie",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 21st International Conference on MultiMedia Modeling, MMM 2015 ; Conference date: 05-01-2015 Through 07-01-2015",
year = "2015",
doi = "10.1007/978-3-319-14442-9_42",
language = "English",
pages = "357--368",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

RIS

TY - GEN

T1 - Discriminative regions

T2 - 21st International Conference on MultiMedia Modeling, MMM 2015

AU - Moghimi, Mohammad

AU - Kerr, Jacqueline

AU - Johnson, Eileen

AU - Godbole, Suneeta

AU - Belongie, Serge

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Life-logging devices are becoming ubiquitous, yet still processing and extracting information from the vast amount of data that is being captured is a very challenging task. We propose a method to find discriminative regions which we define as regions that are salient, consistent, repetitive and discriminative. We explain our fast and novel algorithm to discover the discriminative regions and show different applications for discriminative regions such as summarization, classification and image search. Our experiments show that our algorithm is able to find discriminative regions and discriminative patches in a short time and extracts great results on our life-logging SenseCam dataset.

AB - Life-logging devices are becoming ubiquitous, yet still processing and extracting information from the vast amount of data that is being captured is a very challenging task. We propose a method to find discriminative regions which we define as regions that are salient, consistent, repetitive and discriminative. We explain our fast and novel algorithm to discover the discriminative regions and show different applications for discriminative regions such as summarization, classification and image search. Our experiments show that our algorithm is able to find discriminative regions and discriminative patches in a short time and extracts great results on our life-logging SenseCam dataset.

UR - http://www.scopus.com/inward/record.url?scp=84927792289&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-14442-9_42

DO - 10.1007/978-3-319-14442-9_42

M3 - Conference article

AN - SCOPUS:84927792289

SP - 357

EP - 368

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

Y2 - 5 January 2015 through 7 January 2015

ER -

ID: 301829582