An Efficient Two-layer based Technique for Content-based Image Retrieval


  • Fawzi Abdul Azeez Salih Department of Computer Science, College of Science, University of Sulaimani, Sulaimani,Iraq
  • Alan Anwer Abdulla Department of Information Technology, College of Commerce, University of Sulaimani, Sulaimani, Iraq. Department of Information Technology, University College of Goizha, Sulaimani, Iraq



CBIR, Feature Extraction, Color Descriptor, DWT, LBP


The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.


[1] Z. F. Mohammed and A. A. Abdulla. “Thresholding-based white blood cells segmentation from microscopic blood images”. UHD Journal of Science and Technology, vol. 4, no. 1, p. 9, 2020.
[2] M. W. Ahmed and A. A. Abdulla. “Quality improvement for exemplar-based image inpainting using a modified searching mechanism”. UHD Journal of Science and Technology, vol. 4, no. 1, p. 1, 2020.
[3] H. Liu, J. Yin, X. Luo and S. Zhang. “Foreword to the Special Issue on Recent Advances on Pattern Recognition and Artificial Intelligence”. Springer, Berlin, p. 1, 2018.
[4] A. Wojciechowska, M. Choraś and R. Kozik. “Evaluation of the Pre-processing Methods in Image-Based Palmprint Biometrics”. Springer, International Conference on Image Processing and Communications, p. 1, 2017.
[5] A. A. Abdulla, S. A. Jassim and H. Sellahewa. “Secure Steganography Technique Based on Bitplane Indexes”. 2013 IEEE International Symposium on Multimedia, 2013.
[6] A. A. Abdulla. “Exploiting Similarities between Secret and Cover Images for Improved Embedding Efficiency and Security in Digital Steganography”. Department of Applied Computing, The University of Buckingham, United Kingdom, pp. 1-235, 2015.
[7] S. Farhan, B. K. Biswas and R. Haque. “Unsupervised Content- Based Image Retrieval Technique Using Global and Local Features”. International Conference on Advances in Science, Engineering and Robotics Technology, p. 2, 2019.
[8] R. S. Patil, A. J. Agrawal. “Content-based image retrieval systems: A survey”. Advances in Computational Sciences and Technology, vol. 10, 9, pp. 2773-2788, 2017.
[9] H. Shahadat and R. Islam. “A new approach of content based image retrieval using color and texture features”. Current Journal of Applied Science and Technology, vol. 21, no. 1, pp. 1-16, 2017.
[10] A. Sarwar, Z. Mehmood, T. Saba, K. A. Qazi, A. Adnan and H. Jamal. “A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine”. Journal of Information, vol. 45, pp. 117- 135, 2019.
[11] L. K. Paovthra and S. T. Sharmila. “Optimized feature integration and minimized search space in content based image retrieval”. Procedia Computer Science, vol. 165, pp. 691-700, 2019.
[12] A. Masood, M. A. Shahid and M. Sharif. “Content-based image retrieval features: A survey”. The International Journal of Advanced Networking and Applications, vol. 10, no. 1, pp. 3741- 3757, 2018.
[13] S. Singh and S. Batra. “An Efficient Bi-layer Content Based Image Retrieval System”. Springer, Berlin, p. 3, 2020.
[14] Y. D. Mistry. “Textural and color descriptor fusion for efficient content-based image”. Iran Journal of Computer Science, vol. 3, pp. 1-15, 2020.
[15] K. T. Ahmed, A. Irtaza, M. A. Iqbal. “Fusion of Local and Global Features for Effective Image Extraction”. Elsevier, Amsterdam, Netherlands, vol. 51, pp. 76-99, 2019.
[16] M. O. Divya and E. R. Vimina. “Maximal Multi-channel Local Binary Pattern with Colour Information for CBIR”. Springer, Berlin, p. 2, 2020.
[17] T. Kato. “Database Architecture for Content-based Image Retrieval”. International Society for Optics and Photonics, vol. 1662. pp. 112-123, 1992.
[18] J. Yu, Z. Qin, T. Wan and X. Zhang. “Feature integration analysis of bag-of-features model for image retrieval”. Neurocomputing, vol. 120, pp. 355-364, 2013.
[19] N. Shrivastava. “Content-based Image Retrieval Based on Relative Locations of Multiple Regions of Interest Using Selective Regions Matching”. Elsevier, Amsterdam, Netherlands, vol. 259, pp. 212- 224, 2014.
[20] E. Gupta and R. S. Kushwah. “Combination of Global and Local Features Using DWT with SVM for CBIR in Reliability”. Infocom Technologies and Optimization (ICRITO) Trends and Future Directions, 2015.
[21] E. Gupta and R. S. Kushwah. “Content-based image retrieval through combined data of color moment and texture”. International Journal of Computer Science and Network Security, vol. 17, pp. 94-97, 2017.
[22] 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, p. 4, 2018.
[23] P. Jitesh, A. Ashok, P. A. Kumarand and B. Haider. “Multi-level colored directional motif histograms for content-based”. The Visual Computer, vol. 36, pp. 1847-1868, 2020.
[24] K. T. Ahmed and S. H. Naqvi. “Convolution, Approximation and Spatial Information Based Object and Color Signatures for Content Based Image Retrieval”. 2019 International Conference on Computer and Information Sciences, 2019.
[25] H. Qazanfari, H. Hassanpour and K. Qazanfari. “Content-based image retrieval using HSV color space features”. International Journal of Computer and Information Engineering, vol. 13, no. 10, pp. 537-545, 2019.
[26] E. Rashno. “Content-based image retrieval system with most relevant features among wavelet and color features”. Iran University of Science and Technology, vol. pp. 1-18, 2019.
[27] K. S. Aiswarya, N. Santhi and K. Ramar. “Content-based image retrieval for mobile devices using multi-stage autoencoders”. Journal of Critical Reviews, vol. 7, pp. 63-69, 2020.
[28] J. Zhou, X. Liu, W. Liu and J. Gan. “Image retrieval based on effective feature extraction and diffusion process”. Multimedia Tools and Applications, vol. 78, no. 5, pp. 6163-6190, 2019.
[29] P. Srivastava. “Content-Based Image Retrieval Using Multiresolution Feature Descriptors”. Springer, Berlin, pp. 211-235, 2019.
[30] I. A. Saad. “An efficient classification algorithms for image retrieval based color and texture features”. Journal of AL-Qadisiyah for Computer Science and Mathematics, vol. 10, no. 1, pp. 42-53, 2018.
[31] M. S. Haji. “Content-based image retrieval: A deep look at features prospectus”. International Journal of Computational Vision and Robotics, vol. 9, no. 1, pp. 14-37, 2019.
[32] V. Geetha, V. Anbumani, S. Sasikala and L. Murali. “Efficient Hybrid Multi-level Matching with Diverse Set of Features for Image Retrieval”. Springer, Berlin, pp. 12267-12288, 2020.
[33] R. Boukerma, S. Bougueroua and B. Boucheham. “A Local Patterns Weighting Approach for Optimizing Content-Based Image Retrieval Using a Differential Evolution Algorithm”. 2019 International Conference on Theoretical and Applicative Aspects of Computer Science, 2019.
[34] Y. Cai, G. Xu, A. Li and X. Wang. “A novel improved local binary pattern and its application to the fault diagnosis of diesel engine”. Shock and Vibration, vol. 2020, p. 9830162, 2020.
[35] G. Xie, B. Guo, Z. Huang, Y. Zheng and Y. Yan. “Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval”. IEEE Access, vol. 8, pp. 146284-146299, 2020.
[36] A. C. Nehal and M. Varma. “Evaluation of Distance Measures in Content Based Image Retrieval”. 2019 3rd International conference on Electronics, Communication and Aerospace Technology, pp. 696-701, 2019.
[37] S, Bhardwaj, G. Pandove and P. K. Dahiya. “A futuristic hybrid image retrieval system based on an effective indexing approach for swift image retrieval”. International Journal of Computer Information Systems and Industrial Management Applications, vol. 12, pp. 1-13, 2020.
[38] S. P. Rana, M. Dey and P. Siarry. “Boosting content based image retrieval performance through integration of parametric and nonparametric approaches”. Journal of Visual Communication and Image Representation, vol. 58, pp. 205-219, 2019.
[39] M. K. Alsmadi. “Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features”. Springer, Berlin, pp. 1-14, 2020.