Main Article Content

Shefali Dhingra
Guru Gobind Singh Indraprastha University, New Delhi, India
India
Poonam Bansal
Maharaja Surajmal Institute of Technology, New delhi, India
India
Vol. 8 No. 2 (2019), Articles, pages 33-49
DOI: https://doi.org/10.14201/ADCAIJ2019823349
Accepted: Feb 24, 2020
Copyright

Abstract

To find out the identical or comparable images from the large rotated databases with higher retrieval accuracy and lesser time is the challenging task in Content based Image Retrieval systems (CBIR). Considering this problem, an intelligent and efficient technique is proposed for texture based images. In this method, firstly a new joint feature vector is created which inherits the properties of Local binary pattern (LBP) which has steadiness regarding changes in illumination and rotation and discrete wavelet transform (DWT) which is multi-resolutional and multi-oriented along with higher directionality. Secondly, after the creation of hybrid feature vector, to increase the accuracy of the system, classifiers are employed on the combination of LBP and DWT. The performance of two machine learning classifiers is proposed here which are Support Vector Machine (SVM) and Extreme learning machine (ELM). Both proposed methods P1 (LBP+DWT+SVM) and P2 (LBP+DWT+ELM) are tested on rotated Brodatz dataset consisting of 1456 texture images and MIT VisTex dataset of 640 images. In both experiments the results of both the proposed methods are much better than simple combination of DWT +LBP and much other state of art methods in terms of precision and accuracy when different number of images is retrieved.  But the results obtained by ELM algorithm shows some more improvement than SVM. Such as when top 25 images are retrieved then in case of Brodatz database the precision is up to 94% and for MIT VisTex database its value is up to 96% with ELM classifier which is very much superior to other existing texture retrieval methods.

Downloads

Download data is not yet available.

Article Details

References

Alaei, F., Alaei, A., Pal, U., & Blumenstein, M. (2018). AC PT US CR. Expert Systems With Applications. https://doi.org/10.1016/j.eswa.2018.12.007.

Chamasemani, F. F. (2011). Multi-class Support Vector Machine ( SVM ) classifiers - An Application in Hypothyroid detection and Classification, 353-358. https://doi.org/10.1109/BIC-TA.2011.51.

Chorowski, J., Wang, J., & Zurada, J. M. (2014). Neurocomputing Review and performance comparison of SVM and ELM-based classifiers $, 128, 507-516. https://doi.org/10.1016/j.neucom.2013.08.009.

Das, R., Dash, J. K., & Mukhopadhyay, S. (2013). Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis, 46, 3256-3267. https://doi.org/10.1016/j.patcog.2013.05.026

Ding, S., & Xu, X. (2013). Extreme learning machine: algorithm, theory and applications, (August 2014). https://doi.org/10.1007/s10462-013-9405-z.

Fadaei, S., Amirfattahi, R., & Ahmadzadeh, M. R. (2017). Local derivative radial patterns: A new texture descriptor for content-based image retrieval. Signal Processing, 137, 274-286. https://doi.org/10.1016/j.sigpro.2017.02.013.

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Neurocomputing Deep learning for visual understanding: A review, 187, 27-48. https://doi.org/10.1016/j.neucom.2015.09.116.

Haralick, R. M., & Shanmugam, K. (1973). Textural Features for Image Classification.

Hemachandran, K., Paul, A., & Singha, M. (2012). Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram. IET Image Processing, 6(9), 1221-1226. https://doi.org/10.1049/iet-ipr.2011.0453.

Huang, D., Member, S., Shan, C., & Ardabilian, M. (2011). Local Binary Patterns and Its Application to Facial Image Analysis: A Survey, (November). https://doi.org/10.1109/TSMCC.2011.2118750.

Huang, G., Member, S., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for Regression and Multiclass Classification, 42(2), 513-529.

Jain, A. K., & Vailaya, A. (1995). Image Retrieval using Color and Shape. Pattern Recognition, 29, 1233-1244. https://doi.org/10.1016/0031-3203(95)00160-3.

Karthikeyan, T., & Manikandaprabhu, P. (2014). A Study on Discrete Wavelet Transform based Texture Feature Extraction for Image Mining, 5(5), 1805-1811.

Kastrati, Z., & Imran, A. S. (2019). Performance Analysis of Machine Learning Classifiers on Improved Concept Vector Space Models. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2019.02.006.

Kokare, M., Biswas, P. K., & Chatterji, B. N. (2005). Complex Wavelet Filters, 35(6), 1168-1178.

Kumar, Y., Aggarwal, A., Tiwari, S., & Singh, K. (2018). An efficient and robust approach for biomedical image retrieval using Zernike moments. Biomedical Signal Processing and Control, 39, 459-473. https://doi.org/10.1016/j.bspc.2017.08.018.

Liao, S., Law, M. W. K., & Chung, A. C. S. (2009). for Texture Classification, 18(5), 1107-1118.

Liu, S., Wang, H., Wu, J., & Feng, L. (2015). Incorporate Extreme Learning Machine to content-based image retrieval with relevance feedback, (March). https://doi.org/10.1109/WCICA.2014.7052854.

Lu, B., Duan, X., & Wang, C. (n.d.). A Novel Approach for Image Classification Based on Extreme Learning Machine.

Maheshwari, S. M. R. P., & Balasubramanian, R. (2012). Directional local extrema patterns: a new descriptor for content based image retrieval, 191-203. https://doi.org/10.1007/s13735-012-0008-2.

Murala, S., Maheshwari, R. P., & Balasubramanian, R. (2012). Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval, (May). https://doi.org/10.1109/TIP.2012.2188809.

Naghashi, V. (2018). Optik Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval. Optik - International Journal for Light and Electron Optics, 157, 877-889. https://doi.org/10.1016/j.ijleo.2017.11.160.

Pavithra, L. K., & Sharmila, T. S. (2017).An efficient framework for image retrieval using color, texture and edge features R. Computers and Electrical Engineering, 0, 1-14. https://doi.org/10.1016/j.compeleceng.2017.08.030.

Pham, M. (2017). Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance. https://doi.org/10.3390/jimaging3040043.

Prakasa, E. (2016). Ekstraksi Ciri Tekstur dengan Menggunakan Local Binary Pattern Texture Feature Extraction by Using Local Binary Pattern, 9(2), 45-48. https://doi.org/10.14203/j.inkom.420.

Puviarasan, N., Bhavani, R., & Vasanthi, A. (2014). Image Retrieval Using Combination of Texture and Shape Features, 3(3), 5873-5877.

R, J. V. C. I., Murala, S., & Wu, Q. M. J. (2014). Expert content-based image retrieval system using robust local patterns. Journal Of Visual Communication And Image Representation, 25(6), 1324-1334. https://doi.org/10.1016/j.jvcir.2014.05.008.

Raghuwanshi, G., & Tyagi, V. (2015). Texture image retrieval using adaptive tetrolet transforms. Digital Signal Processing, 1(3), 1-8. https://doi.org/10.1016/j.dsp.2015.09.003.

Ricardo, A., Joaci, J., & Sá, D. M. (2017). Neurocomputing LBP maps for improving fractal based texture classification, 266, 1-7. https://doi.org/10.1016/j.neucom.2017.05.020.

Sreena, P. H., & George, D. S. (2013). Content Based Image Retrieval System with Fuzzified Texture Similarity Measurement, (Iccc), 80-85.

Srivastava, P., & Khare, A. (2017). Utilizing multiscale local binary pattern for content-based image retrieval. https://doi.org/10.1007/s11042-017-4894-4.

Wang, H., Feng, L., Zhang, J., & Liu, Y. (2016). Semantic discriminative metric learning for image similarity measurement. IEEE Transactions on Multimedia, 18(8), 1579-1589. https://doi.org/10.1109/TMM.2016.2569412.

Zhao, R., & Grosky, W. I. (2000). From features to semantics: some preliminary results. Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on, 2(c), 679-682 vol.2. https://doi.org/10.1109/ICME.2000.871453.