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Content-based feature fusion representation for marine invertebrates

Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Mo...

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Main Authors: Mustaffa, Mas Rina, Mohd Norowi, Noris, Sim, May Yee
Format: Article
Language:English
Published: Faculty of Computer Science and Information Technology 2020
Online Access:http://psasir.upm.edu.my/id/eprint/88254/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88254/
https://ejournal.um.edu.my/index.php/MJCS/article/view/25273
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spelling my.upm.eprints.882542022-11-24T04:11:10Z http://psasir.upm.edu.my/id/eprint/88254/ Content-based feature fusion representation for marine invertebrates Mustaffa, Mas Rina Mohd Norowi, Noris Sim, May Yee Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Most of the current methods only support content-based representation for marine life images with clear background (taken in laboratory or in environments which have been set up), containing just one animal in an image, or the animal is positioned nicely at the centre of the image. Responding to these important needs, a multi-feature method for Content-based Image Retrieval (CBIR) that employs colour, shape, and texture information of marine life images is proposed. The colour feature vectors are obtained by extracting first and second order of Colour Moments. Shape information is constructed through the implementation of Discrete Wavelet transform up to four sub-bands and the extraction of Canny edge feature. Texture features are obtained with the Zernike Moments (ZM) of order four and the extraction of few Grey Level Co-occurrence Matrix properties. We conducted two experiments to determine the best order of ZM as well as to measure the retrieval performance of the proposed descriptor. Retrieval results based on marine invertebrate and Fish4Knowledge datasets clearly shown that the proposed method has effectively obtained the best precision value at 11 standard recall levels (72.42%) and MAP value (67.7%). The proposed method is further measured based on the statistical two-tailed paired t-test and has revealed a significant improvement in retrieval effectiveness. Faculty of Computer Science and Information Technology 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/88254/1/ABSTRACT.pdf Mustaffa, Mas Rina and Mohd Norowi, Noris and Sim, May Yee (2020) Content-based feature fusion representation for marine invertebrates. Malaysian Journal of Computer Science, 33 (3). 170 - 187. ISSN 0127-9084 https://ejournal.um.edu.my/index.php/MJCS/article/view/25273
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 Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Most of the current methods only support content-based representation for marine life images with clear background (taken in laboratory or in environments which have been set up), containing just one animal in an image, or the animal is positioned nicely at the centre of the image. Responding to these important needs, a multi-feature method for Content-based Image Retrieval (CBIR) that employs colour, shape, and texture information of marine life images is proposed. The colour feature vectors are obtained by extracting first and second order of Colour Moments. Shape information is constructed through the implementation of Discrete Wavelet transform up to four sub-bands and the extraction of Canny edge feature. Texture features are obtained with the Zernike Moments (ZM) of order four and the extraction of few Grey Level Co-occurrence Matrix properties. We conducted two experiments to determine the best order of ZM as well as to measure the retrieval performance of the proposed descriptor. Retrieval results based on marine invertebrate and Fish4Knowledge datasets clearly shown that the proposed method has effectively obtained the best precision value at 11 standard recall levels (72.42%) and MAP value (67.7%). The proposed method is further measured based on the statistical two-tailed paired t-test and has revealed a significant improvement in retrieval effectiveness.
format Article
author Mustaffa, Mas Rina
Mohd Norowi, Noris
Sim, May Yee
spellingShingle Mustaffa, Mas Rina
Mohd Norowi, Noris
Sim, May Yee
Content-based feature fusion representation for marine invertebrates
author_facet Mustaffa, Mas Rina
Mohd Norowi, Noris
Sim, May Yee
author_sort Mustaffa, Mas Rina
title Content-based feature fusion representation for marine invertebrates
title_short Content-based feature fusion representation for marine invertebrates
title_full Content-based feature fusion representation for marine invertebrates
title_fullStr Content-based feature fusion representation for marine invertebrates
title_full_unstemmed Content-based feature fusion representation for marine invertebrates
title_sort content-based feature fusion representation for marine invertebrates
publisher Faculty of Computer Science and Information Technology
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/88254/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88254/
https://ejournal.um.edu.my/index.php/MJCS/article/view/25273
_version_ 1751538217243377664
score 13.211869

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