Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction
: Image Processing
(University of Sistan and Baluchestan)
(دانشگاه سیستان و بلوچستان)
Tile and Ceramic,
Accuracy and Speed of Retrieval,
One of the most important databases in the e-commerce is tile and ceramic database, for which no specific retrieval method has been provided so far. In this paper, a method is proposed for the content-based retrieval of digital images of tiles and ceramics databases. First, a database is created by photographing different tiles and ceramics on the market from different angles and directions, including 520 images. Then a query image and the database images are divided into nine equal sub-images and all are grouped based on their sub-images. Next, the selected color and texture features are extracted from the sub-images of the database images and query image, so, each image has a feature vector. The selected features are the minimum features that are required to reduce the amount of computations and information stored, as well as speed up the retrieval. Average precision is calculated for the similarity measure. Finally, comparing the query feature vector with the feature vectors of all database images leads to retrieval. According to the retrieving results by the proposed method, its accuracy and speed are improved by 16.55% and 23.88%, respectively, compared to the most similar methods.
 T. Mehyar, and J. O. Atoum, “An enhancement on content-based image retrieval using color and texture features”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 3, No. 4, 2012, pp. 488-496.
 C. M. Ibraheem and G. U. Reddy, “Content based image retrieval using HSV color, shape, and GLCM texture”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 10, 2015, pp. 1-6.
 P. Sharma, and D. Dubey, “Color averaging technique using dominant color for content based image retrieval”, International Journal of Computer Science, Vol. 10, No. 3, 2013, pp. 603-607.
 Ahmed J. Afifi, and Wesam M. Ashour, “Content-based image retrieval using invariant color and texture features”, in International Conference on Digital Image Computing Techniques and Applications, 2012, pp. 1-6. DOI:10.1109/DICTA.2012.6411665.
 T. Prathiba, N. M. Mary Sindhuja, and S. Nisharani, “Content based image retrieval based on spatial constraints using Lab view”, International Journal of Engineering Research and Technology, Vol. 2, No. 1, 2013, pp. 1-6.
 V. Khandave, and N. Mishra, “CBIR by integration of color and texture features”, International Journal of Recent Development in Engineering and Technology, Vol. 2, No. 1, 2014, PP. 1-6.
 R. K. Lingadalli and N. Ramesh, “Content based image retrieval using color, shape and texture”, International Advanced Research Journal in Science, Engineering and Technology, Vol. 2, No. 6, 2015, pp. 40-48.
 A. Huneiti, and M. Daoud, “Content-based image retrieval using SOM and DWT”, Journal of Soft Engineering and Applications, Vol. 8, 2015, pp. 51-61.
 S. Kaur, and N. Kaur, “Content based image retrieval using color histogram and Wavelet based color histogram algorithms”, International Journal of Engineering Research and General Science, Vol. 4, No. 3, 2016, pp. 530-535.
 R. Bulli Babu, V. Vanitha, and K. Sai Anish, “Content based image retrieval using color, texture, shape and active re-ranking method”, Indian Journal of Science and Technology, Vol. 9, No. 17, 2016, pp. 1-5.
 N. Jain, and S.S. Salankar, “Content based image retrieval using combined color and texture features”, IOSR Journal of Electrical and Electronics Engineering, Vol. 11, No. 6, 2016, pp. 53-58.
 H. H. Bu, N. C. Kim, C. J. Moon, and J. H. Kim, “Content-based image retrieval using multi-resolution multi-direction filtering-based CLBP texture features and color autocorrelogram features”, Journal of Information Processing Systems, Vol.16, No. 4, 2020, pp. 991-1000.
 M. B. Suresh, and B. Mohankumar Naik, “Content based image retrieval using texture structure histogram and texture features”, International Journal of Computational Intelligence Research, Vol. 13, No. 9, 2017, pp. 2237-2245.
 M. B. Suresh, and B. Mohankumar Naik, “Content based image retrieval using color and texture content”, International Journal of Computer Trends and Technology, Vol. 48, No. 2, 2017, pp. 78-84.
 D. Sarala, T. Kanikdaley, S. Jogi, and R. K. Chaurasiya, “Content-based image retrieval using hierarchical color and texture similarity calculation”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 7, No. 2, 2018, pp. 11-16.
 J. Q. Alnihoud, “Image retrieval system based on color global and local features combined with GLCM for texture features”, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 9, 2018, pp. 164-171.
 J. Pradhan, S. Kumar, A. Kumarpal, and H. Banka, “A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features”, Digital Signal Processing, Vol. 82, 2018, pp. 258-281.
 S. Unar, X. Wang, C. Wang, and Y. Wang, “A decisive content based image retrieval approach for feature fusion in visual and textual images”, Journal of Knowledge-Based Systems, Vol. 179, 2019, pp. 8-20.
 K. T. Ahmed, S. Ummesafi, and M. Iqbal, “Content based image retrieval using image features information fusion”, Journal of Information Fusion, Vol. 51, 2019, pp.76-99.
 N. Hor and S. Fekri-Ershad, “Image retrieval approach based on local texture information derived from predefined patters and spatial domain information”, International Journal of Computer Science Engineering, Vol. 8, No. 6, 2019, pp.246-254.
 W. Xiong, Z. Xiong, Y. Zhang, Y. Cui, and X. Gu, “A deep cross-modality Hashing network for SAR and optical remote sensing images retrieval”, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, Vol. 13, 2020, pp. 5284-5296.
 H. Zhang, M. Jiang, and Q. Kou, “Color image retrieval algorithm fusing color and principal curvatures information”, IEEE Access, Vol. 8, 2020, pp. 184945-184954.
 D. Niu, X. Zhao, X. Lin, and C. Zhang, “A novel image retrieval method based on multi-features fusion”, Signal Processing: Image Communication, Vol. 87, 2020.
 M. Garg and G. Dhiman, “A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variations”, Neural Computing and Applications, Vol. 33, 2021, pp. 1311-1328.
 N. Varish, A. Kumar Pal, R. Hassan, M. K. Hasan, A. Khan, N. Parveen, D. Banerjee, V. Pellakuri, N. Ul Haq, and I. Memon, “Image retrieval scheme using quantized bins of color image components and adaptive Tetrolet transform”, IEEE Access, Vol. 8, 2020, pp. 117639-117665.
 Hussain Dawood, M. H. Alkinani, A. Raza, Hassan Dawood, R. Mehboob, and S. Shabbir, “Correlated microstructure descriptor for image retrieval”, IEEE Access, Vol. 7, 2019, pp. 55206-55228.
 Ali Ahmed, “Implementing relevance feedback for content-based medical image retrieval”, IEEE Access, Vol. 8, 202, pp. 79969-79976.
 L. K. Pavithra, and T. Sree Sharmila, “An improved seed point selection based unsupervised color clustering for content-based image retrieval application”, Computer Journal, 2020. DOI:10.1093/Comjnl/bxz017.
 K. T. Ahmed, S. Aslam, H. Afzal, S. Iqbal, A. Mehmood, and G. S. Choi, “Symmetric image contents analysis and retrieval using decimation, pattern analysis, orientation, and features fusion”, IEEE Access, Vol.9, 2021, pp. 57215-57242.
 S. Ram Dubey, “A decade survey of content based image retrieval using deep learning”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.32, No. 5, 2021, pp. 2687-2704.