Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors

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Md. Farhan Sadique 1,* S M Rafizul Haque 1

1. Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

* Corresponding author.


Received: 7 Dec. 2019 / Revised: 20 Dec. 2019 / Accepted: 25 Dec. 2019 / Published: 8 Jun. 2020

Index Terms

Color layout descriptor, gray-level co-occurrence matrix, KNN, corel-1k, dominant color, scale invariant


Content-based image retrieval (CBIR) is the process of retrieving similar images of a query image from a source of images based on the image contents. In this paper, color and texture features are used to represent image contents. Color layout descriptor (CLD) and gray-level co-occurrence matrix (GLCM) are used as color and texture features respectively. CLD and GLCM are efficient for representing images with local dominant regions. For retrieving similar images of a query image, the features of the query image is matched with that of the images of the source. We use cityblock distance for this feature matching purpose. K-nearest images using cityblock distance are the similar images of a query image. Our CBIR approach is scale invariant as CLD is scale invariant. Another set of features, GLCM defines color patterns. It makes the system efficient for retrieving similar images based on spatial relationships between colors. We also measure the efficiency of our approach using k-nearest neighbors algorithm. Performance of our proposed method, in terms of precision and recall, is promising and better, compared to some recent related works.

Cite This Paper

Md. Farhan Sadique, S M Rafizul Haque, "Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.3, pp.19-25, 2020. DOI:10.5815/ijitcs.2020.03.03


[1]D. A. Kumar and J. Esther, “Comparative study on CBIR based by color histogram, Gabor and wavelet transform,” International Journal of Computer Applications, vol. 17, no. 3, pp. 37-44, 2011.

[2]S. Laencina Verdaguer, “Color based image classification and description,” MS thesis, Universitat Politcnica de Catalunya, 2009.

[3]E. Gose, “Pattern recognition and image analysis,” Second printing, p. 159, 2000.

[4]J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.

[5]J. Z. Wang, J. Li and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.

[6]J. Han, M. Kamber and J. Pei, “Data mining: concepts and techniques,” Elsevier, p. 368, 2011.

[7]S. Somnugpong and K. Khiewwan, “Content-based image retrieval using a combination of color correlograms and edge direction histogram,” 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016.

[8]A. Nazir, R. Ashraf, T. Hamdani and N. Ali, “Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor,” 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018.

[9]J. A. da Silva Junior, R. Elias Marcal and M. Aurelio Batista, “Image retrieval: importance and applications,” Workshop de Visao Computacional - WVC, 2014.

[10]M. Tzelepi and A. Tefas, “Deep convolutional learning for content based image retrieval,” Neurocomputing, vol. 275, pp. 2467-2478, 2018.

[11]T, Kato, “Database architecture for content-based image retrieval,” in image storage and retrieval systems, vol. 1662, pp. 112-123, International Society for Optics and Photonics, 1992.

[12]S.M. M. Islam and R. Debnath, “An RST invariant image retrieval approach using color moments and wavelet packet entropy,” 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 747-752, 2016.

[13]M. F. Sadique, B. K. Biswas and S.M. R. Haque, “Unsupervised content-based image retrieval technique using global and local features,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019.

[14]P. Srivastava and A. Khare, “Content-based image retrieval using scale invariant feature transform and moments,” 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), 2016.

[15]F. Ye, M. Dong, W. Luo, X. Chen and W. Min, “A new re-ranking method based on convolutional neural network and two image-to-class distances for remote sensing image retrieval,” IEEE Access, pp. 141498-141507, 2019.