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BoVW model for animal recognition: an evaluation on SIFT feature strategies

Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features...

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Main Authors: Mansourian, Leila, Abdullah, Muhamad Taufik, Abdullah, Lili Nurliyana, Azman, Azreen, Mustaffa, Mas Rina
Other Authors: Zaman, Halimah Badioze
Format: Book Section
Language:English
Published: Springer International Publishing 2015
Online Access:http://psasir.upm.edu.my/id/eprint/47154/1/abstract02.pdf
http://psasir.upm.edu.my/id/eprint/47154/
http://download.springer.com/static/pdf/560/chp%253A10.1007%252F978-3-319-25939-0_20.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-25939-0_20&token2=exp=1467005822~acl=%2Fstatic%2Fpdf%2F560%2Fchp%25253A10.1007%25252F978-3-31
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spelling my.upm.eprints.471542016-06-27T06:01:48Z http://psasir.upm.edu.my/id/eprint/47154/ BoVW model for animal recognition: an evaluation on SIFT feature strategies Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lili Nurliyana Azman, Azreen Mustaffa, Mas Rina Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results. Springer International Publishing Zaman, Halimah Badioze Robinson, Peter Smeaton, Alan F. Shih, Timothy K. Velastin, Sergio Jaafar, Azizah Mohamad Ali, Nazlena 2015 Book Section PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47154/1/abstract02.pdf Mansourian, Leila and Abdullah, Muhamad Taufik and Abdullah, Lili Nurliyana and Azman, Azreen and Mustaffa, Mas Rina (2015) BoVW model for animal recognition: an evaluation on SIFT feature strategies. In: Advances in Visual Informatics: 4th International Visual Informatics Conference, IVIC 2015, Bangi, Malaysia, November 17-19, 2015, Proceedings. Lecture Notes in Computer Science (9429). Springer International Publishing, Switzerland, pp. 227-236. ISBN 9783319259383; EISBN: 9783319259390 http://download.springer.com/static/pdf/560/chp%253A10.1007%252F978-3-319-25939-0_20.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-25939-0_20&token2=exp=1467005822~acl=%2Fstatic%2Fpdf%2F560%2Fchp%25253A10.1007%25252F978-3-31 10.1007/978-3-319-25939-0_20
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results.
author2 Zaman, Halimah Badioze
author_facet Zaman, Halimah Badioze
Mansourian, Leila
Abdullah, Muhamad Taufik
Abdullah, Lili Nurliyana
Azman, Azreen
Mustaffa, Mas Rina
format Book Section
author Mansourian, Leila
Abdullah, Muhamad Taufik
Abdullah, Lili Nurliyana
Azman, Azreen
Mustaffa, Mas Rina
spellingShingle Mansourian, Leila
Abdullah, Muhamad Taufik
Abdullah, Lili Nurliyana
Azman, Azreen
Mustaffa, Mas Rina
BoVW model for animal recognition: an evaluation on SIFT feature strategies
author_sort Mansourian, Leila
title BoVW model for animal recognition: an evaluation on SIFT feature strategies
title_short BoVW model for animal recognition: an evaluation on SIFT feature strategies
title_full BoVW model for animal recognition: an evaluation on SIFT feature strategies
title_fullStr BoVW model for animal recognition: an evaluation on SIFT feature strategies
title_full_unstemmed BoVW model for animal recognition: an evaluation on SIFT feature strategies
title_sort bovw model for animal recognition: an evaluation on sift feature strategies
publisher Springer International Publishing
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/47154/1/abstract02.pdf
http://psasir.upm.edu.my/id/eprint/47154/
http://download.springer.com/static/pdf/560/chp%253A10.1007%252F978-3-319-25939-0_20.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-25939-0_20&token2=exp=1467005822~acl=%2Fstatic%2Fpdf%2F560%2Fchp%25253A10.1007%25252F978-3-31
_version_ 1643833857104936960
score 13.211869

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