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Content-based image retrieval system for plant leaf database using texture

Automatic plant leaf images retrieval system help the students and the researchers in botany field. It does so by overcoming limitations associated by the system such as the domain knowledge requirement and the time consumption. It also helps in learning process where the retrieval will speed up the...

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Main Author: Hussein, Ahmed Naser
Format: Thesis
Language:English
Published: 2011
Online Access:http://psasir.upm.edu.my/id/eprint/41795/1/FK%202011%2020R.pdf
http://psasir.upm.edu.my/id/eprint/41795/
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spelling my.upm.eprints.417952016-03-01T02:35:50Z http://psasir.upm.edu.my/id/eprint/41795/ Content-based image retrieval system for plant leaf database using texture Hussein, Ahmed Naser Automatic plant leaf images retrieval system help the students and the researchers in botany field. It does so by overcoming limitations associated by the system such as the domain knowledge requirement and the time consumption. It also helps in learning process where the retrieval will speed up the search of any plant species and gives better experience to the students to familiarize themselves with the plant species. The motivation of this work was driven by inherent difficulties of the manual classification of plant leaf images. To achieve that, an automatic, fast, and robust content based image retrieval (CBIR) system is designed. The richness and uniqueness of plant leaf texture is used in this work as a principal feature in classifying the plant leaf species. A study on the texture extraction approach for plant leaf image is crucial in designing an effective image retrieval system.To classify plant leaf image, CBIR system is employed which extract the leaf texture and then use the extracted feature to compare against the gallery for similarity measurement. The texture extraction is accomplished using Discrete Wavelet Transformation (DWT) incorporating with entropy measurement which enhances the classification of images. The dataset for this experimental work has been obtained from the American National Herbarium Collections. The dataset offers wide diversity of rotation, noise, luminance and scale on plant leaf image. The experiments have been performed on seven plant species that consist of 280 images. To evaluate the robustness of the system, the experiments are repeated on 92 species that consist of 3597 images. The proposed framework yields a correct classification rate of 92.5% , 85.92% of average precision rate for top 5 images, 71.9% of average recall rate for top 100 images, length of feature vector is 36, and the average retrieval time is 1.0656 seconds only on overall system framework. The results were compared with another CBIR system which is based on Gray Level Co-occurrence Matrix (GLCM) and then showed better performance in terms of evaluation of images classification and retrieval. 2011-04 Thesis NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41795/1/FK%202011%2020R.pdf Hussein, Ahmed Naser (2011) Content-based image retrieval system for plant leaf database using texture. Masters thesis, Universiti Putra Malaysia.
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 Automatic plant leaf images retrieval system help the students and the researchers in botany field. It does so by overcoming limitations associated by the system such as the domain knowledge requirement and the time consumption. It also helps in learning process where the retrieval will speed up the search of any plant species and gives better experience to the students to familiarize themselves with the plant species. The motivation of this work was driven by inherent difficulties of the manual classification of plant leaf images. To achieve that, an automatic, fast, and robust content based image retrieval (CBIR) system is designed. The richness and uniqueness of plant leaf texture is used in this work as a principal feature in classifying the plant leaf species. A study on the texture extraction approach for plant leaf image is crucial in designing an effective image retrieval system.To classify plant leaf image, CBIR system is employed which extract the leaf texture and then use the extracted feature to compare against the gallery for similarity measurement. The texture extraction is accomplished using Discrete Wavelet Transformation (DWT) incorporating with entropy measurement which enhances the classification of images. The dataset for this experimental work has been obtained from the American National Herbarium Collections. The dataset offers wide diversity of rotation, noise, luminance and scale on plant leaf image. The experiments have been performed on seven plant species that consist of 280 images. To evaluate the robustness of the system, the experiments are repeated on 92 species that consist of 3597 images. The proposed framework yields a correct classification rate of 92.5% , 85.92% of average precision rate for top 5 images, 71.9% of average recall rate for top 100 images, length of feature vector is 36, and the average retrieval time is 1.0656 seconds only on overall system framework. The results were compared with another CBIR system which is based on Gray Level Co-occurrence Matrix (GLCM) and then showed better performance in terms of evaluation of images classification and retrieval.
format Thesis
author Hussein, Ahmed Naser
spellingShingle Hussein, Ahmed Naser
Content-based image retrieval system for plant leaf database using texture
author_facet Hussein, Ahmed Naser
author_sort Hussein, Ahmed Naser
title Content-based image retrieval system for plant leaf database using texture
title_short Content-based image retrieval system for plant leaf database using texture
title_full Content-based image retrieval system for plant leaf database using texture
title_fullStr Content-based image retrieval system for plant leaf database using texture
title_full_unstemmed Content-based image retrieval system for plant leaf database using texture
title_sort content-based image retrieval system for plant leaf database using texture
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/41795/1/FK%202011%2020R.pdf
http://psasir.upm.edu.my/id/eprint/41795/
_version_ 1643833101916307456
score 13.211869

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