Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

Ezekiel Mensah Martey, Hang Lei, Xiaoyu Li, Obed Appiah


Image representation is critical to the successful realisation of Content Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous, and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance.

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Deselaers T., Keysers D. & Ney H. (2008). Features for image retrieval: an experimental comparison. Inf. Retrieval 11(2), 77–107, 2008.

Pass G. & Zabith R. (1996). Histogram refinement for content-based image retrieval, in Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 96–102,

Srivastava P., Binh N. T., & Khare A. (2013). Content-based image retrieval using moments”. In International Conference on Context-Aware Systems and Applications (pp. 228-237), Springer, Cham.

Huang J., Kumar S., Mitra M., Zhu W. J, &. Zabih R. (1997). Image indexing using colour correlograms. In Proceedings of IEEE computer society conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997.

Tyagi V., (2018). Content-based image retrieval: ideas, influences, and current trends. Springer

Rao A., Srihari R. K., & Zhang Z. (1999). Spatial colour histograms for content-based image retrieval”. In Proceedings 11th International Conference on Tools with Artificial Intelligence (pp. 183-186), IEEE.

Kurtz C., Depeursinge A., Napel S., Beaulieu C. F.,. & Rubin D. L. (2014). On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Medical image analysis, 18(7), 1082-1100.

Tuceryan M., & Jain A. K.(1993). Texture analysis. Handbook of pattern recognition and computer vision, pp. 235-276, 1993.

Park D. K, Jeon Y. S., & Won C. S.(2000). Efficient use of local edge histogram descriptor. In Proceedings of the 2000 ACM workshops on Multimedia, pp. 51-54.

Ojala T.M., & Pietikainen T.M. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971–987.

Marula S., Maheshwari R.P., & Balasubramanian R. (2012). Local tetra pattern: A new feature descriptor for content-based image retrieval”. IEEE Trans. Image Process, vol 21, pp 2874–2886, doi:10.1109/TIP.2012.2188809.

Robert M., Haralick K., & Shanmugam I. D. (1973).Textural features for image classification”. IEEE Trans. Syst. Man Cybern, vol 3, pp 610–621, doi:10.1109/TSMC.1973.4309314.

Zhang D., Wong A., Indrawan M., & Lu G. (2000). Content-based image retrieval using Gabor texture features. IEEE Transactions Pami, 3656, 13âĂŞ15.

Manjunath B. S., & Ma W. Y.(1996). Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell, vol 18, pp 837–842, 1996.

Sumana I. J., Islam M. M., Zhang D. & Lu G. (2008). Content-based image retrieval using curvelet transform. In 2008 IEEE 10th workshop on multimedia signal processing (pp. 11-16).

Cohen F.S., Fan Z. G., & Patel M. A. (1991). Classification of rotated and scaled textured images using Gaussian Markov random field models. IEEE Trans. Pattern Anal. Mach. Intell. vol 13, 192–202.

Julesz B.(1986). Texton gradients: The texton theory revisited. Biological Cybernetics, vol 54(4), pp 245-251.

Liu G. H., Zhang L., Hou Y. K., Li Z. Y. & Yang J. Y.(2010). Image retrieval based on multi-texton histogram. Pattern Recognition, vol 43(7), pp.2380-2389.

Liu G. H. & Yang J. Y. (2008). Image retrieval based on the texton co-occurrence matrix. Pattern Recognition, vol 41(12), 3521-3527.

Kumari Y. S., Kumar V. V. & Satyanarayana C. (2017). Texture classification using complete texton matrix. International Journal of Image, Graphics and Signal Processing, vol 9(10), 60.

Khaldi B., Aiadi O., & Lamine K. M. (2020). Image representation using complete multi-texton histogram. Multimedia Tools and Applications, vol 79(11), pp 8267-8285.

Tyagi V. (2017). Content-Based Image Retrieval Using a Short Run Length Descriptor. In Content-Based Image Retrieval; Springer: Singapore, pp. 241–256.

Martey E. M., Lei H., Li X. & Appiah O. (2021). Image Representation Using Stacked Colour Histogram. Algorithms, vol 14(8), 228, 2021.

Liu G. H., Li Z. Y., Zhang L. & Xu Y.(2011). Image retrieval based on the micro-structure descriptor. Pattern Recognit. Vol. 44, 2123–2133.

Liu G. H., Zhang L. & Li Z.Y. (2015). Content-based image retrieval using computational visual attention model. Pattern Recognit, 48, 2554–2566.

Liu, G.H. & Yang J.Y. (2008). Image retrieval based on the texton co-occurrence matrix. Pattern Recogn. Vol 41, pp. 3521–3527.

G.H. Liu, and J.Y. Yang, “Content-based image retrieval using colour difference histogram”. Pattern Recognit, vol 46, 188–198, 2013.

Xingyuan W. & Zongyu W. (2013). A novel method for image retrieval based on structure elements descriptor. J. Vis. Commun. Image Represent. 24, 63–74.

Tian X., Jiao L., Liu X., & Zhang X. (2014). Feature integration of eodh and colour-sift: Application to image retrieval based on codebook, Signal Processing: Image Communication, vol. 29, no. 4, pp. 530–545, 2014.

Dubey S. R., Singh S. K, & Singh R. K. (2015). Rotation and scale-invariant hybrid image descriptor and retrieval, Computers & Electrical Engineering, vol. 46, pp. 288–302.

Nazir, A. & Kashif N. (2018). An efficient image retrieval based on fusion of low-level visual features. arXiv preprint arXiv:1811.12695.

Zafar B., Ashraf R., Ali N., Ahmed M., Jabbar S. & Chatzichristofis S. A.(2018). Image classification by addition of spatial information based on histograms of orthogonal vectors, PloS one, vol. 13, p. e0198175.

Martey E. M., Lei H., Li X., Appiah O. & Awarayi N. S. (2021). Evaluation of RGB Quantization Schemes on Histogram-Based Content-Based Image Retrieval. In International Conference on Artificial Intelligence and Security, pp. 736-747.

Appiah O., Martey E. M. & Quayson E. (2019). Effect of Window's Shape on Median Filtering. In Proceedings of the 2019 IEEE AFRICAN, Accra, Ghana, pp. 1–8.

Minarno A.E., Munarko Y., Bimantoro F., Kurniawardhani A. & Suciati N. (2014). Batik image retrieval based on enhanced micro-structure descriptor. In Proceedings of the 2014 Asia-Pacific Conference on Computer-Aided System Engineering (APCASE), South Kuta, Indonesia, pp. 65–70.

Mori G., Belongie S. & Malik J. (2001). Shape contexts enable efficient retrieval of similar shapes. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, Volume 1.

Khaldi B., Aiadi O. & Kherfi M. L. (2019) “Combining colour and grey‐level co‐occurrence matrix feature: A comparative study”. IET Image Process, vol13, pp. 1401–1410.

Bala A. & Kaur, T. (2016). Local texton XOR patterns: A new feature descriptor for content-based image retrieval. Eng. Sci. Technol. Int. J., vol 19, 101–112.

DOI: https://doi.org/10.31449/inf.v45i7.3715

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