Multi-texture classification using optimized Gabor Filter by Artificial Bee Colony
Global Proceedings Repository
View Archive Info| Field | Value | |
| Title |
Multi-texture classification using optimized Gabor Filter by Artificial Bee Colony
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| Creator |
Alsadegh Saleh Saied Mohamed
Joan Lu Qiang Xu |
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| Description |
Texture classification is an important topic which is used in many applications of computer vision. The Gabor Bank is one of the well-known feature extraction methods for texture classification. However, Gabor Bank suffers from high dimensional features which can be solved by finding a suitable filter for a particular task and that is the concern of this research. This paper introduces a new optimization method (Artificial Bee Colony (ABC)) to automatically select the proper values of the Gabor Filter parameters in order to design the optimal filter and avoid high dimensional features. The parameters values are tuned according to texture groups for classification. The texture groups have been prepared from standard database of University of Maryland Database (UMD). In experimental results, the average classification rate was comparable with 88.0438% of Gabor Bank against 82.7938 % of optimal filter. The optimal GF can be improved by integrating with other optimized methods.
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| Publisher |
Int'l Conference on Change, Innovation, Informatics and Disrurptuive Technology
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| Contributor |
—
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| Date |
2016-12-17 20:52:44
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| Type |
Peer-reviewed Paper
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| Format |
application/pdf
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| Identifier |
http://proceedings.sriweb.org/repository/index.php/ICCIIDT/icciidtt_london/paper/view/18
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| Source |
Int'l Conference on Change, Innovation, Informatics and Disrurptuive Technology; ICCIIDT London - UK
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| Language |
en
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| Rights |
Authors who submit to this conference agree to the following terms: a) Authors retain copyright over their work, while allowing the conference to place this unpublished work under a Creative Commons Attribution License, which allows others to freely access, use, and share the work, with an acknowledgement of the work's authorship and its initial presentation at this conference. b) Authors are able to waive the terms of the CC license and enter into separate, additional contractual arrangements for the non-exclusive distribution and subsequent publication of this work (e.g., publish a revised version in a journal, post it to an institutional repository or publish it in a book), with an acknowledgement of its initial presentation at this conference. c) In addition, authors are encouraged to post and share their work online (e.g., in institutional repositories or on their website) at any point before and after the conference. |
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