A Novel Content-based Image Retrieval System using Fusing Color and Texture Features | ||
| Journal of AI and Data Mining | ||
| دوره 10، شماره 4، بهمن 2022، صفحه 559-568 اصل مقاله (1.53 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.12042.2353 | ||
| نویسندگان | ||
| S. Asadi Amiri* 1؛ Z. Mohammadpoory2؛ M. Nasrolahzadeh3 | ||
| 1Department of Computer Engineering, University of Mazandaran, Babolsar, Iran. | ||
| 2Department of Electronic and biomedical Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| 3Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Image retrieval؛ Color histogram؛ Texture feature؛ DCT؛ lbp | ||
| مراجع | ||
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