A Novel Content-based Image Retrieval System using Fusing Color and Texture Features

Asadi Amiri, S.; Mohammadpoory, Z.; Nasrolahzadeh, M.

doi:10.22044/jadm.2022.12042.2353

Document Type : Technical Paper

Authors

¹ Department of Computer Engineering, University of Mazandaran, Babolsar, Iran.

² Department of Electronic and biomedical Engineering, Shahrood University of Technology, Shahrood, Iran.

³ Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.

https://doi.org/10.22044/jadm.2022.12042.2353

Abstract

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.

Keywords

20.1001.1.23225211.2022.10.4.10.4

Main Subjects

H.5. Image Processing and Computer Vision

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A Novel Content-based Image Retrieval System using Fusing Color and Texture Features

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Volume 10, Issue 4November 2022Pages 559-568

Volume 10, Issue 4
November 2022
Pages 559-568