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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Book Section |
| Language: | English |
| Published: |
Springer International Publishing
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47154/1/abstract02.pdf |
| _version_ | 1796974766451589120 |
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| author | Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lili Nurliyana Azman, Azreen Mustaffa, Mas Rina |
| author2 | Zaman, Halimah Badioze |
| author_facet | Zaman, Halimah Badioze Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lili Nurliyana Azman, Azreen Mustaffa, Mas Rina |
| author_sort | Mansourian, Leila |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T09:01:46Z |
| format | Book Section |
| id | upm.eprints-47154 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:01:46Z |
| publishDate | 2015 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | BoVW model for animal recognition: an evaluation on SIFT feature strategies
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| 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_short | BoVW model for animal recognition: an evaluation on SIFT feature strategies
|
| title_sort | bovw model for animal recognition an evaluation on sift feature strategies |
| url | http://psasir.upm.edu.my/id/eprint/47154/1/abstract02.pdf |
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