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Content-Based Image Retrieval Based on Texture and Color Combinations Using Tamura Texture Features and Gabor Texture Methods
American Journal of Neural Networks and Applications
Volume 5, Issue 1, June 2019, Pages: 23-27
Received: Apr. 22, 2019; Accepted: Jun. 24, 2019; Published: Jul. 4, 2019
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Bahtiar Imran, Sekolah Tinggi Manajemen Informatika Komputer Mataram (STMIK Mataram), Mataram, Indonesia
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As the development of data storage technology from various sources of information then increasingly cause problems in the search and processing, of course with the existence of this problem will be feared can cause big losses. Various existing search techniques have not been able to provide clear results between query testing and training. To overcome this problem required a Content-Based Image Retrieval (CBIR) approach which is a technique for content-based image search. In this study using texture and color information from image training to present the results both in query testing and database training using Tamura texture method features Gabor texture features. Before displaying the query results first the image testing in extracts using Tamura texture features and Gabor texture features to get the feature values used for image testing and then matching it with the value of features available in the training database. The application used in this research is an application from LIRE and database image that used is database from image. orig. the results obtained in this research is, the application of texture Tamura method features and Gabor texture features based on the features and colors can provide significant results between image testing and image training.
CBIR, Feature, Tamura Texture Feature, Texture, Matching
To cite this article
Bahtiar Imran, Content-Based Image Retrieval Based on Texture and Color Combinations Using Tamura Texture Features and Gabor Texture Methods, American Journal of Neural Networks and Applications. Vol. 5, No. 1, 2019, pp. 23-27. doi: 10.11648/j.ajnna.20190501.14
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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