Abstract
Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.
Similar content being viewed by others
References
Irtaza, A.; Adnan, S.M.; Ahmed, K.T.; Jaffar, A.; Khan, A.; Javed, A.; Mahmood, M.T.: An ensemble based evolutionary approach to the class imbalance problem with applications in CBIR. Appl. Sci. 8(4), 495 (2018)
Alsmadi, M.K.: An efficient similarity measure for content based image retrieval using memetic algorithm. Egypt. J. Basic Appl. Sci. 4, 112–122 (2017)
Alsmadi, M.K.: Query-sensitive similarity measure for content-based image retrieval using meta-heuristic algorithm. J. King Saud Univ. Comput. Inf. Sci. 30, 373–381 (2018)
Shashank, J.; Kowshik, P.; Srinathan, K.; Jawahar, C.: Private content based image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8
Radwan, A.A.; Latef, B.A.A.; Ali, A.M.A.; Sadek, O.A.: Using genetic algorithm to improve information retrieval systems. World Acad. Sci. Eng. Technol. 17(2), 6–13 (2006)
Carneiro, G.; Chan, A.B.; Moreno, P.J.; Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007)
Das, R.; Thepade, S.; Bhattacharya, S.; Ghosh, S.: Retrieval architecture with classified query for content based image recognition. Appl. Comput. Intell. Soft Comput. 2016, 2 (2016)
Ashraf, R.; Bashir, K.; Irtaza, A.; Mahmood, M.T.: Content based image retrieval using embedded neural networks with bandletized regions. Entropy 17(6), 3552–3580 (2015)
Zeng, Z.: A novel local structure descriptor for color image retrieval. Information 7(1), 9 (2016)
Zhou, J.-X.; Liu, X.-D.; Xu, T.-W.; Gan, J.-H.; Liu, W.-Q.: A new fusion approach for content based image retrieval with color histogram and local directional pattern. Int. J. Mach. Learn. Cybern. 9(4), 677–689 (2018)
Nazir, A.; Ashraf, R.; Hamdani, T.; Ali, N.: Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 3–4 March 2018, pp. 1–6
Feng, L.; Wu, J.; Liu, S.; Zhang, H.: Global correlation descriptor: a novel image representation for image retrieval. J. Vis. Commun. Image Represent. 33, 104–114 (2015)
Madhavi, K.V.; Tamilkodi, R.; Sudha, K.J.: An innovative method for retrieving relevant images by getting the top-ranked images first using interactive genetic algorithm. Procedia Comput. Sci. 79, 254–261 (2016)
Ali, N.; Bajwa, K.B.; Sablatnig, R.; Chatzichristofis, S.A.; Iqbal, Z.; Rashid, M.; Habib, H.A.: A novel image retrieval based on visual words integration of SIFT and SURF. PLoS ONE 11(6), e0157428 (2016)
Goldberg, D.E.: Genetic algorithms in search. Optim. Mach. Learn. 3(1), 372 (1989)
Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Aarts, E.; Korst, J.: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. Wiley, Hoboken (1988)
Pirim, H.; Bayraktar, E.; Eksioglu, B.: Tabu Search: A Comparative Study. InTech, Croatia (2008)
Xhafa, F.; Abraham, A.: Metaheuristics for scheduling in distributed computing environments, vol 146. Springer (2008)
Li, Y.; Guo, H.; Wang, L.; Fu, J.: A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under E-supply chain environment. Sci. World J. 2013, 1–10 (2013)
Liang, W.; Tang, M.; Jing, L.; Sangaiah, A.K.; Huang, Y.: SIRSE: a secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Comput. Electr. Eng. 65, 310–321 (2018)
Samuel, O.W.; Zhou, H.; Li, X.; Wang, H.; Zhang, H.; Sangaiah, A.K.; Li, G.: Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Comput. Electr. Eng. 67, 646–655 (2018)
Ashraf, R.; Ahmed, M.; Jabbar, S.; Khalid, S.; Ahmad, A.; Din, S.; Jeon, G.: Content based image retrieval by using color descriptor and discrete wavelet transform. J. Med. Syst. 42(3), 44 (2018)
Acharya, T.; Ray, A.K.: Image processing: principles and applications. Wiley (2005)
Wang, X.-Y.; Zhang, B.-B.; Yang, H.-Y.: Content-based image retrieval by integrating color and texture features. Multimed. Tools Appl. 68(3), 545–569 (2014)
Müller, H.; Michoux, N.; Bandon, D.; Geissbuhler, A.: A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)
Syam, B.; Rao, Y.: An effective similarity measure via genetic algorithm for content based image retrieval with extensive features. Int. Arab J. Inf. Technol. (IAJIT) 10(2), 143–151 (2013)
Alsmadi, M.; Omar, K.B.; Noah, S.A.; Almarashdeh, I.: Fish recognition based on robust features extraction from size and shape measurements using neural network. J. Comput. Sci. 6(10), 1088–1094 (2010)
Badawi, U.A.; Alsmadi, M.K.: A general fish classification methodology using meta-heuristic algorithm with back propagation classifier. J. Theor. Appl. Inf. Technol. 66(3), 803–812 (2014)
Alias, M.S.A.; Ibrahim, N.; Zin, Z.M.: Enhanced median filter for low density salt and pepper noise removal in lead frame image. Int. J. Appl. Eng. Res. 12(24), 14638–14644 (2017)
Shanmugavadivu, P.; Shanthasheela, A.: Feature variance based filter for speckle noise removal. IOSR J. (IOSR J. Comput. Eng.) 1(16), 15–19 (2014)
Alsmadi, M.K.: A hybrid Fuzzy C-means and neutrosophic for jaw lesions segmentation. Ain Shams Eng. J. 9, 697–706 (2017)
Guo, Y.; Sengur, A.: NCM: neutrosophic c-means clustering algorithm. Pattern Recognit. 48(8), 2710–2724 (2015)
Benco, M.; Hudec, R.; Kamencay, P.; Zachariasova, M.; Matuska, S.: An advanced approach to extraction of colour texture features based on GLCM. Int. J. Adv. Robot. Syst. 11, 1–8 (2014)
Alsmadi, M.K.; Omar, K.B.; Noah, S.A.; Almarashdeh, I.: Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier. J. Theor. Appl. Inf. Technol. 18(1), 11–18 (2010)
Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)
Nikoo, H.; Talebi, H.; Mirzaei, A.: A supervised method for determining displacement of gray level co-occurrence matrix. In: Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1–5
Haralick, R.M.; Shanmugam, K.; Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Khan, G.M.: Evolutionary computation. In: Kacprzyk, J. (ed.). Evolution of Artificial Neural Development. Springer, pp. 29–37 (2018)
Varun Kumar, S.; Panneerselvam, R.: A study of crossover operators for genetic algorithms to solve VRP and its variants and new sinusoidal motion crossover operator. Int. J. Comput. Intell. Res. 13(7), 1717–1733 (2017)
Pirim, H.; Bayraktar, E.; Eksioglu, B.: Tabu Search: A Comparative Study. In: Jaziri, W. (ed.). InTech, Croatia, pp. 1–28 (2008)
Xhafa, F.; Abraham, A.: Metaheuristics for Scheduling in Distributed Computing Environments. Springer, Berlin (2008)
Oliveira, R.; Candeias, T.; Santos, L.; Shahbazkia, H.: Gabor filters optimized by simple Simulated Annealing. In: Proceedings of RECPAD (2000)
Tsaia, D.-M.; Wua, S.-K.; Chenb, M.-C.: Optimal Gabor ®lter design for texture segmentation using stochastic optimization. Image Vis. Comput. 19, 299–316 (2001)
Moscato, P.: Memetic algorithms: a short introduction. In: David, C., Marco, D., Fred, G., Dipankar, D., Pablo, M., Riccardo, P., Kenneth, V.P. (eds.) New ideas in optimization, pp. 219–234. McGraw-Hill Ltd., London (1999)
Mehmood, Z.; Mahmood, T.; Javid, M.A.: Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine. Appl. Intell. 48(1), 166–181 (2018)
Ali, N.; Bajwa, K.B.; Sablatnig, R.; Mehmood, Z.: Image retrieval by addition of spatial information based on histograms of triangular regions. Comput. Electr. Eng. 54, 539–550 (2016)
Mehmood, Z., Anwar, S.M., Ali, N., Habib, H.A., Rashid, M.A.: novel image retrieval based on a combination of local and global histograms of visual words. Math. Probl. Eng. 2016, 1–12 (2016)
Zeng, S.; Huang, R.; Wang, H.; Kang, Z.: Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models. Neurocomputing 171, 673–684 (2016)
Walia, E.; Pal, A.: Fusion framework for effective color image retrieval. J. Vis. Commun. Image Represent. 25(6), 1335–1348 (2014)
Wang, C.; Zhang, B.; Qin, Z.; Xiong, J.: Spatial weighting for bag-of-features based image retrieval. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 91–100
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Alsmadi, M.K. Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features. Arab J Sci Eng 45, 3317–3330 (2020). https://doi.org/10.1007/s13369-020-04384-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-04384-y