Tropical Wood Species Recognition Based on Gabor Filter
Tropical timber woods have more than 1,000 species. Some of the species have similar patterns with others and some have different patterns even though they are of the same species. One of the main problems in wood species recognition system is the lack of discriminative features of the texture image...
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creator | Yusof, R. Rosli, N.R. Khalid, M. |
description | Tropical timber woods have more than 1,000 species. Some of the species have similar patterns with others and some have different patterns even though they are of the same species. One of the main problems in wood species recognition system is the lack of discriminative features of the texture images. Gabor filter has been extensively used as feature extractor for various applications such as face detection, face recognition, image retrieval and font type extraction. In our work, we propose the use of Gabor filter to generate multiple processed images from a single image so that more features can be extracted and will be trained by neural network. The use of Gabor filters will optimally localized the properties of the images in both spatial and frequency domain. The features of the filtered images are extracted using cooccurrence matrix approach, known as grey level cooccurrence matrix (GLCM). A multi-layer neural network based on the popular BP (back propagation) algorithm is used for classification. The results show that increasing the number of features by means of Gabor filters as well as the right combination of Gabor filters increases the accuracy rate of the system. |
doi_str_mv | 10.1109/CISP.2009.5302660 |
format | Conference Proceeding |
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Some of the species have similar patterns with others and some have different patterns even though they are of the same species. One of the main problems in wood species recognition system is the lack of discriminative features of the texture images. Gabor filter has been extensively used as feature extractor for various applications such as face detection, face recognition, image retrieval and font type extraction. In our work, we propose the use of Gabor filter to generate multiple processed images from a single image so that more features can be extracted and will be trained by neural network. The use of Gabor filters will optimally localized the properties of the images in both spatial and frequency domain. The features of the filtered images are extracted using cooccurrence matrix approach, known as grey level cooccurrence matrix (GLCM). A multi-layer neural network based on the popular BP (back propagation) algorithm is used for classification. 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The results show that increasing the number of features by means of Gabor filters as well as the right combination of Gabor filters increases the accuracy rate of the system.</description><subject>Face detection</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Frequency domain analysis</subject><subject>Gabor filters</subject><subject>Image generation</subject><subject>Image recognition</subject><subject>Image retrieval</subject><subject>Multi-layer neural network</subject><subject>Neural networks</subject><isbn>1424441293</isbn><isbn>9781424441297</isbn><isbn>1424441315</isbn><isbn>9781424441310</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9j91KAzEUhCNS0NY-gHiTF9j1JCfZbC51sbVQUGzFy5KfsxJYm2V3b3x7KxavZga-GRjGbgWUQoC9bza711IC2FIjyKqCCzYXSiqlBAp9-R-kxRmb_4IWBEpzxZbjmPyporXVRl8zsx9yn4Lr-EfOke96ColG_kYhfx7TlPKRP7qRIj-ZtfN54KvUTTTcsFnrupGWZ12w99XTvnkuti_rTfOwLZLQ1VRUQXnACk1tgQBrF0MkVUN0oKNEqyTYqMijaC261pGn1viI3mKtHLa4YHd_u4mIDv2QvtzwfTi_xh8LyEho</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Yusof, R.</creator><creator>Rosli, N.R.</creator><creator>Khalid, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>Tropical Wood Species Recognition Based on Gabor Filter</title><author>Yusof, R. ; Rosli, N.R. ; Khalid, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i156t-6c4b03637890e038adcde480da05d2394209d4eb31f93afaebef7bd3b9384a3f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng ; jpn</language><creationdate>2009</creationdate><topic>Face detection</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Frequency domain analysis</topic><topic>Gabor filters</topic><topic>Image generation</topic><topic>Image recognition</topic><topic>Image retrieval</topic><topic>Multi-layer neural network</topic><topic>Neural networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Yusof, R.</creatorcontrib><creatorcontrib>Rosli, N.R.</creatorcontrib><creatorcontrib>Khalid, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yusof, R.</au><au>Rosli, N.R.</au><au>Khalid, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Tropical Wood Species Recognition Based on Gabor Filter</atitle><btitle>2009 2nd International Congress on Image and Signal Processing</btitle><stitle>CISP</stitle><date>2009-10</date><risdate>2009</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><isbn>1424441293</isbn><isbn>9781424441297</isbn><eisbn>1424441315</eisbn><eisbn>9781424441310</eisbn><abstract>Tropical timber woods have more than 1,000 species. Some of the species have similar patterns with others and some have different patterns even though they are of the same species. One of the main problems in wood species recognition system is the lack of discriminative features of the texture images. Gabor filter has been extensively used as feature extractor for various applications such as face detection, face recognition, image retrieval and font type extraction. In our work, we propose the use of Gabor filter to generate multiple processed images from a single image so that more features can be extracted and will be trained by neural network. The use of Gabor filters will optimally localized the properties of the images in both spatial and frequency domain. The features of the filtered images are extracted using cooccurrence matrix approach, known as grey level cooccurrence matrix (GLCM). A multi-layer neural network based on the popular BP (back propagation) algorithm is used for classification. The results show that increasing the number of features by means of Gabor filters as well as the right combination of Gabor filters increases the accuracy rate of the system.</abstract><pub>IEEE</pub><doi>10.1109/CISP.2009.5302660</doi><tpages>5</tpages></addata></record> |
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subjects | Face detection Face recognition Feature extraction Frequency domain analysis Gabor filters Image generation Image recognition Image retrieval Multi-layer neural network Neural networks |
title | Tropical Wood Species Recognition Based on Gabor Filter |
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