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Journal of Artificial Intelligence and Data Mining، جلد ۱۰، شماره ۴، صفحات ۵۵۹-۵۶۸
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عنوان انگلیسی A Novel Content-based Image Retrieval System using Fusing Color and Texture Features
چکیده انگلیسی مقاله Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces and texture features are represented by localized Discrete Cosine Transform (DCT) and localized Gray level co-occurrence matrix and local binary patterns (LBP). The DCT coefficients and Gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, Shannon entropy criterion is used to reduce inefficient features. Finally, an improved version of Canberra distance is employed to compare similarity of feature vectors. Experimental analysis is carried out using precision and recall on Corel-5K and Corel-10K datasets. Results demonstrate that the proposed method can efficiently improve the precision and recall and outperforms the most existing methods.s the most existing methods.
کلیدواژه‌های انگلیسی مقاله Image Retrieval, Color histogram, Texture feature, DCT, LBP
نویسندگان مقاله S. Asadi Amiri |
Department of Computer Engineering, University of Mazandaran, Babolsar, Iran.

Z. Mohammadpoory |
Department of Electronic and biomedical Engineering, Shahrood University of Technology, Shahrood, Iran.

M. Nasrolahzadeh |
Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.

نشانی اینترنتی https://jad.shahroodut.ac.ir/article_2506_7f0d2a86f3d838dcb90e1277ab0db515.pdf
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