New Dominant Color Descriptor Features Based on Weighting of More Informative Pixels using Suitable Masks for Content-Based Image Retrieval

Document Type : Original Article


Department of Electrical Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran


Content-based image retrieval (CBIR) is a process of retrieving images based on their content in a dataset automatically. CBIR is a common solution to search images similar to a desired image among all images in dataset. To do this, many methods have been developed to extract images features. Here, a new Dominant Color Descriptor (DCD) method is proposed to improve CBIR accuracy. In the first step, Canny edges of images are extracted. In the next step, edges are widened by employing morphological operations. Finally, pixels that are not at the edges are weighted less than the pixels which are located at edges. Indeed, pixels in regions with low color variations are less weighted and more informative pixels are more weighted in providing DCD features. To show the effectiveness of the proposed method, experiments are performed on three datasets Corel-1k, Corel-10k and Caltech256. Results demonstrate that the proposed method outperforms competitive methods.


Main Subjects

  1. Pavithra, L. K., and Sharmila, T. S., “Optimized feature integration and minimized search space in content based image retrieval”, Procedia Computer Science, Vol. 165, (2019), 691-700, doi: 1016/j.procs.2020.01.065.
  2. Fadaei, S., Amirfattahi, R., and Ahmadzadeh, M. R., “Local derivative radial patterns: A new texture descriptor for content-based image retrieval”, Signal Processing, Vol. 137, (2017), 274-286, doi: 1016/j.sigpro.2017.02.013.
  3. Dubey, S.R., Singh, S.K., and Singh, R.K., “Local neighbourhood-based robust colour occurrence descriptor for colour image retrieval”, IET Image Processing, Vol. 9, No. 7, (2015), 578-586, doi: 1049/iet-ipr.2014.0769.
  4. Singha, M., Hemachandran, K., and Paul, A., “Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram”, IET Image Processing, Vol. 6, No. 9, (2012), 1221-1226, doi: 1049/iet-ipr.2011.0453
  5. Pass, G. and Zabih, R., “Histogram refinement for content-based image retrieval”, Proc. 3rd IEEE Workshop on Applications of Computer Vision (WACV’96), Sarasota, FL, (1996), 96-102, doi: 1109/ACV.1996.572008.
  6. Chun, Y.D., Kim, N.C., and Jang, I.H., “Content-based image retrieval using multiresolution color and texture features”, IEEE Transactions on Multimedia, Vol. 10, No. 6, (2008), 1073-1084, doi: 1109/TMM.2008.2001357.
  7. Wang, X.-Y., Yu, Y.-J., and Yang, H.-Y., “An effective image retrieval scheme using color, texture and shape features”, Computer Standard Interfaces, Vol. 33, No. 1, (2011), 59-68, doi: 1016/j.csi.2010.03.004.
  8. Talib, A., Mahmuddin, M., Husni, H., and George, L. E., “A weighted dominant color descriptor for content-based image retrieval”, Journal of Visual Communication and Image Representation, Vol. 24, No. 3, (2013), 345-360, doi: 1016/j.jvcir.2013.01.007.
  9. Fadaei, S., Amirfattahi, R., and Ahmadzadeh, M. R., “New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features”, IET Image Processing, Vol. 11, No. 2, (2017), 89-98, doi: 1049/iet-ipr.2016.0542.
  10. Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., and Shin, H., “An efficient color representation for image retrieval”, IEEE Transactions on Image Processing, Vol. 10, No. 1, (2001) 140-147, doi: 1109/83.892450.
  11. Wang, X. Y., Yu, Y. J., and Yang, H. Y., “An effective image retrieval scheme using color, texture and shape features”, Computer Standards & Interfaces, Vol. 33, No. 1, (2011), 59-68, doi: 1016/j.csi.2010.03.004.
  12. Po, L. M., and Wong, K. M., “A new palette histogram similarity measure for MPEG-7 dominant color descriptor”, In 2004 International Conference on Image Processing (ICIP’04), IEEE, (2004), 1533-1536, doi: 1109/ICIP.2004.1421357.
  13. Mojsilovic, A., Kovacevic, J., Hu, J., Safranek, R. J., and Ganapathy, S. K., “Matching and retrieval based on the vocabulary and grammar of color patterns”, IEEE Transactions on Image Processing, Vol. 9, No. 1, (2000), 38-54, doi: 1109/83.817597.
  14. Fadaei, S., and Rashno, A., “Content-based image retrieval speedup based on optimized combination of wavelet and zernike features using particle swarm optimization algorithm”, International Journal of Engineering, Transactions B: Applications, Vol. 33, No. 5, (2020), 1000-1009, doi: 5829/IJE.2020.33.05B.34.
  15. Pavithra, L. K., and Sharmila, T. S., “An efficient seed points selection approach in dominant color descriptors (DCD)”, Cluster Computing, Vol. 22, No. 4, (2019), 1225-1240, doi: 10.1007/s10586-019-02907-3.
  16. Xie, G., Baolong, G., Zhe, H., Yan, Z., and Yunyi Y., “Combination of dominant color descriptor and Hu moments in consistent zone for content-based image retrieval”, IEEE Access, Vol. 8, (2020), 146284-146299, doi: 1109/ACCESS.2020.3015285.
  17. Sezavar, A., Farsi, H., and Mohamadzadeh, S., “A modified grasshopper optimization algorithm combined with cnn for content-based image retrieval”, International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 7, (2019), 924-930, DOI: 5829/IJE.2019.32.07A.04.
  18. Irtaza, A., Adnan, S. M., Ahmed, K. T., Jaffar, A., Khan, A., Javed, A., and Mahmood, M. T., “An ensemble based evolutionary approach to the class imbalance problem with applications in CBIR”, Applied Sciences, Vol. 8, No. 4, (2018), 495, doi: 3390/app8040495.
  19. Mehmood, Z., Mahmood, T., and Javid, M. A., “Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine”, Applied Intelligence, Vol. 48, No. 1, (2018), 166-181, doi: 1007/s10489-017-0957-5.
  20. Ali, N., Bajwa, K. B., Sablatnig, R., and Mehmood, Z., “Image retrieval by addition of spatial information based on histograms of triangular regions”, Computers & Electrical Engineering, Vol. 54, (2016), 539-550, doi: 1016/j.compeleceng.2016.04.002.
  21. Mehmood, Z., Anwar, S. M., Ali, N., Habib, H. A., and Rashid, M., “A novel image retrieval based on a combination of local and global histograms of visual words”, Mathematical Problems in Engineering, (2016), doi: 1155/2016/8217250.
  22. Zeng, S., Huang, R., Wang, H., and Kang, Z., “Image retrieval using spatiograms of colors quantized by gaussian mixture models”, Neurocomputing, Vol. 171, (2016), 673-684, doi: 1016/j.neucom.2015.07.008.
  23. Walia, E., and Pal, A., “Fusion framework for effective color image retrieval”, Journal of Visual Communication and Image Representation, Vol. 25, No. 6, (2014), 1335-1348, doi: 1016/j.jvcir.2014.05.005.
  24. Wang, C., Zhang, B., Qin, Z., and Xiong, J., “Spatial weighting for bag-of-features based image retrieval”, In International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, Springer, Berlin, Heidelberg, (2013), 91-100, doi: 1007/978-3-642-39515-4_8.
  25. Alsmadi, M. K., “Content-based image retrieval using color, shape and texture descriptors and features”, Arabian Journal for Science and Engineering, Vol. 45, No. 4, (2020), 3317-3330, doi: 10.1007/s13369-020-04384-y.