Abstract
The content-based image retrieval (CBIR) system accepts the input in the form of images and retrieves the relevant images from the database. The CBIR system automatically extracts the prominent key information from the image involved in the retrieval task. The color is one of the key information of the image and it is represented by dominant color descriptors (DCD). Here, similar colors get clustered and the mean value of each cluster represents the dominant color. The random number of unstable cluster formation in DCD alleviates the CBIR system performance. The proposed work has minimized the drawback of DCD by introducing seed points selection based on the mean, maximum and minimum value of the color pixels present in the image. Moreover, this work suggests the optimal cluster number by validating the different combinations of the proposed stable dominant color clusters. The retrieval precision of the proposed CBIR has improved since this work gives equal weight for both the dominant color and its occurrence probability in distance metric calculation. Finally, four standard datasets namely Wang’s, Corel-10k, OT-scene, and Oxford flower are considered for evaluation, and it gives more number of relevant images compared to the state-of-the-art dominant color feature extraction techniques used on these datasets.
Similar content being viewed by others
References
Jiang, X., Li, C., Sun, J.: A modified k-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-0949-6
Ye, M., Johns, E., Walter, B., Meining, A., Yang, G.Z.: An image retrieval framework for real-time endoscopic image retargeting. Int. J. CARS. 12, 1281 (2017). https://doi.org/10.1007/s11548-017-1620-7
Abdullah, S.L.S., Hambali, H.A., Jamil, N.: Segmentation of natural images using an improved thresholding-based technique. Procedia Eng. 41, 938–944 (2012)
Alkhalaf, S., Alfarraj, O., Hemeida, A.M.: Fuzzy-VQ image compression based hybrid PSOGSA optimization algorithm. In: IEEE International Conference on Fuzzy Systems (FUZZIEEE), pp. 1–6 (2015)
Equitz, W.H.: A new vector quantization clustering algorithm. IEEE Trans. Acoust. Speech Signal Process. 37(10), 1568–1575 (1989)
EmreCelebi, M.: Improving the performance of k-means for color quantization. Image Vis. Comput. 29(4), 260–271 (2011)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, pp. 281–297 (1967)
Bai, C., Zhang, J., Liu, Z., Zhao, W.L.: K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimed. Tools Appl. 74, 1469–1488 (2015)
Agrawal, S.C., Jalal, A.S., Tripathi, R.K.: A hybrid method for image categorization using shape descriptors and histogram of oriented gradients. In: Proceedings of International Conference on Computer Vision and Image Processing, pp. 285–295 (2017)
Han, C.: Improved SLIC imagine segmentation algorithm based on K-means. Clust. Comput. 20, 1017–1023 (2017). https://doi.org/10.1007/s10586-017-0792-9
Pei, J., Zhao, L., Dong, X., Dong, X.: Effective algorithm for determining the number of clusters and its application in image segmentation. Clust. Comput. 20, 2845–2854 (2017). https://doi.org/10.1007/s10586-017-1083-1
Zhou, Y., Ren, Q.: Fuzzy c-means clustering algorithm for performance improvement of ENN. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1346-x
Chen, S.X., Li, F.W., Zhu, W.L., Zhang, T.Q.: Initial codebook algorithm of vector quantization. IEICE Trans. Inf. Syst. E91-D(8), 2189–2191 (2008)
Katsavounidis, I., Kuo, C.C.J., Zhang, Z.: A new initialization technique for generalized Lloyd iteration. IEEE Signal Process. Lett. 1(10), 144–146 (1994)
Lai, J.Z.C., Liaw, Y.C., Liu, J.: A fast VQ codebook generation algorithm using code word displacement. Pattern Recogn. 41(1), 315–319 (2008)
Wang, L., Lu, Z.M., Ma, L.H., Feng, Y.P.: VQ codebook design using modified K-means algorithm with feature classification and grouping based initialization. Multimed. Tools Appl. 77–7, 8495–8510 (2018). https://doi.org/10.1007/s11042-017-4747-1
Sajjad, M., Ullah, A., Ahmad, J., Abbas, N., Rho, S., Baik, S.W.: Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimed. Tools Appl. 77, 4769–4789 (2018). https://doi.org/10.1007/s11042-017-5010-5
Fadaei, S., Amirfattahi, R., Ahmadzadeh, M.R.: New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Process. 11(2), 89–98 (2017)
Yang, N.C., Chang, W.H., Kuo, C.M., Li, T.H.: A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval. J. Vis. Commun. Image Represent. 19, 92–105 (2008). https://doi.org/10.1016/j.jvcir.2007.05.003
Pavithra, L.K., Sree Sharmila, T.: Retrieval of homogeneous images using appropriate color space selection. In: International Conference on Computational Intelligence in Data Mining, pp. 739–747 (2017). https://doi.org/10.1007/978-981-10-3874-7_70
Clausi, D.: K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation. Pattern Recogn. Lett. 35(9), 1959–1972 (2002)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Liu, G.-H., Yang, J.-Y., et al.: Content-based image retrieval using computational visual attention model. Pattern Recogn. 48(8), 2554–2566 (2015)
Nilsback, M.-E., Zisserman, A.: A visual vocabulary for flower classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1447–1454 (2006)
Kassambara, A.: Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning (Multivariate Analysis), vol. 1. STHDA, 1st edn (2017)
Ma, W.Y., Deng, Y., Manjunath, B.S.: Tools for texture/color based search of images. In: SPIE Conference on Human Vision and Electronic Imaging II, pp. 496–507 (1997)
Pavithra, L.K., Sree Sharmila, T.: An efficient framework for image retrieval using color, texture and edge features. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.08.030
Mojsilovic, A., Kovacevic, J., Hu, J., Safranek, R.J., Kicha Ganapathy, S.: Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Trans. Image Process. 9(1), 38–54 (2000)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pavithra, L.K., Sree Sharmila, T. An efficient seed points selection approach in dominant color descriptors (DCD). Cluster Comput 22, 1225–1240 (2019). https://doi.org/10.1007/s10586-019-02907-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-019-02907-3