Image mining based on deep belief neural networkand feature matching approachusing manhattan distance

• Autorzy: Ahmad Faiyaz , Ahmad Tanvir

Identyfikatory

DOI

10.24423/cames.323

• Języki publikacji: EN

Abstrakty

EN

Over the past few decades multimedia content, particularly digital images, has increased at a rapid pace, with several complex images being uploaded to various social websites such as Instagram, Facebook and Twitter. Therefore, it is difficult to search and retrieve the relevant image in seconds. Search engines retrieve images based on traditional text-based methods that depend on metadata and captions. In the last few years, a wide range of research has focused on content-based image retrieval (CBIR) based on image mining approaches. This is a challenging research area due to the ever-increasing multimedia database and other image libraries. In order to offer an effective search and retrieval, a novel CBIR system is proposed using the image mining-based deep belief neural network (IMDBN) technique. The proposed method is designed to enhance retrieval accuracy while diminishing the semantic gap between human visual understanding and image feature representation. To achieve this objective, the proposed system carries several steps like preprocessing, feature extraction, classification, and feature matching. Initially, the input database images are fed into the proposed image mining-based CBIR system, whereas colour-shape-texture (CST) feature extraction technique is applied to extract relevant feature set. The extracted features are fused and stored in the feature vector and are subjected to the proposed IMDBN classification step to retrieve similar images in one label. Whenever a new query content is created, the most relevant images are retrieved. This, in turn, achieves 94% accuracy, which is higher than in existing approaches.

Słowa kluczowe

EN

mining; deep belief neural network; Manhattan distance; median filter; energy; correlation; contrast; dissimilarity; homogeneity

PL

górnictwo; sieć neuronowa głębokiego przekonania; odległość Manhattanu; filtr medianowy; energia; korelacja; kontrast; odmienność; jednorodność

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2021
• Tom: Vol. 28, no. 2
• Strony: 139--167
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Ahmad Faiyaz

• Department of Computer Engineering, Jamia Millia Islamia Central University, New Delhi, India

autor

Ahmad Tanvir

• Department of Computer Engineering, Jamia Millia Islamia Central University, New Delhi, India

Bibliografia

• 1. Q. Zheng, M. Yang., J. Yang, Q. Zhang, X. Zhang, Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process, IEEE Access , 6 : 15844-15869, 2018, doi: 10.1109/ACCESS.2018.2810849.
• 2. Q. Zheng, X. Tian, N. Jiang, M Yang, Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network, Journal of Intelligent & Fuzzy Systems , 37 (4): 5641-5654, 2019, doi: 10.3233/JIFS-190861.
• 3. T.A. Ahmed, S. Ummesafi, A. Iqbal, Content based image retrieval using image features information fusion, Information Fusion , 51 : 76-99, doi: 10.1016/j.inffus.2018.11.004.
• 4. M.S. Haji, M.H. Alkawaz, A. Rehman, T. Saba, Content-based image retrieval: A deep look at features prospectus, International Journal of Computational Vision and Robotics , 9 (1): 14-38, 2019, doi: 10.1504/IJCVR.2019.098004.
• 5. Q. Zheng, X. Tian, M. Yang, H. Wang,Differential learning: a powerful tool for interactive content-based image retrieval, Engineering Letters , 27 (1): 202-215, 2019.
• 6. S.P. Rana, M. Dey, P. Siarry, Boosting content based image retrieval performance through integration of parametric & nonparametric approaches, Journal of Visual Communication and Image Representation , 58 : 205-219, 2019, doi: 10.1016/j.jvcir.2018.11.015.
• 7. U. Chaudhuri, B. Banerjee, A. Bhattacharya, Siamese graph convolutional network for content based remote sensing image retrieval, Computer Vision and Image Understanding , 184 : 22-30, 2019, doi: 10.1016/j.cviu.2019.04.004.
• 8. R.R. Saritha, V. Paul, P.G. Kumar, Content based image retrieval using deep learning process, Cluster Computing , 22 (2): 4187-4200, 2019, doi: 10.1007/s10586-018-1731-0.
• 9. U. Sharif, Z. Mehmood, T. Mahmood, M.A. Javid, A. Rehman, T. Saba, Scene analysis and search using local features and support vector machine for effective content-based image retrieval, Artificial Intelligence Review , 52 (2): 901-925, 2019, doi: 10.1007/s10462-018-9636-0.
• 10. A. Rashno, E. Rashno, Content-based image retrieval system with most relevant features among wavelet and color features, arXiv preprint , arXiv:1902.02059, [eess.IV] , 2019.
• 11. P. Tschandl, G. Argenziano, M. Razmara, J. Yap, Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features, British Journal of Der- matology , 181 (1): 155-165, 2019.
• 12. A. Sarwar, Z. Mehmood, T. Saba, K.A. Qazi, A. Adnan, H. Jamal, A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine, Journal of Information Science , 45 (1): 117-135, 2019, doi: 10.1177/0165551518782825.
• 13. Y.D. Mistry, Textural and color descriptor fusion for efficient content-based image retrieval algorithm, Iran Journal of Computer Science , 3 (3): 169-183, 2020, doi: 10.1007/s42044-020-00056-0.
• 14. R. Ashraf, M. Ahmed, U. Ahmad, M.A. Habib, S. Jabbar, K. Naseer, MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features, Multimedia Tools and Applications , 79 (13): 8553-8579, 2020, doi: 10.1007/s11042-018-5961-1.
• 15. N.F. Haq, M. Moradi, Z.J. Wang, A deep community based approach for large scale content based X-ray image retrieval, Medical Image Analysis , 68 : 101847, doi: 10.1016/j.media.2020.101847.
• 16. A.R. Zubair, O.A. Alo, Content-based image retrieval system using second-order statistics, International Journal of Computer Applications , 176 (36), 9 pages, 2020, doi: 10.5120/ijca2020920475.
• 17. H.-H. Bu, N.-C. Kim, B.-J. Yun, S.-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 , 16 (4), 991-1000, 2020, doi: 10.3745/JIPS.02.0138.
• 18. G. Zhao, M. Zhang, J. Liu, Y. Li, J.-R. Wen, AP-GAN: Adversarial patch attack on content-based image retrieval systems, Geoinformatica , 1-31, 2020, doi: 10.1007/s10707-020-00418-7.
• 19. S. Singh, S. Batra, An efficient bi-layer content based image retrieval system, Multimedia Tools & Applications , 79 (25/26): 17731-17759, 2020.
• 20. G. Xie, B. Guo, Z. Huang, Y. Zheng, Y. Yan, Combination of dominant color descriptor and Hu moments in consistent zone for content based image retrieval, IEEE Access , 8 : 146284-146299, 2020, doi: 10.1109/ACCESS.2020.3015285.
• 21. P. Kavitha, P.V. Saraswathi, Segmentation for content based satellite image retrieval using fuzzy clustering, International Journal of Advanced Science and Technology , 29 (9s): 3042-3049, 2020.
• 22. G. Rudrappa, N. Vijapur, Cloud classification using K-means clustering and content based image retrieval technique, [in]: 2020 International Conference on Communication and Signal Processing (ICCSP) , pp. 0700-0704, 2020, doi: 10.1109/ICCSP48568.2020.9182211.
• 23. X.-Y. Wang, B.-B. Zhang, H.-Y. Yang, Content-based image retrieval by integrating color and texture features, Multimedia Tools and Applications , 68 (3): 545-569, 2014.
• 24. T. Kato, Database architecture for content-based image retrieval, [in:] Image storage and retrieval systems , International Society for Optics and Photonics, Vol. 1662, pp. 112-123, 1992.
• 25. H.-H. Bu, N.-C. Kim, C.-J. Moon, J.-H. Kim, Content-based image retrieval using combined color and texture features extracted by multi-resolution multi-direction filtering, Journal of Information Processing Systems , 13 (3): 464-475, 2017, doi: 10.3745/ JIPS.02.0060.
• 26. J.J. Thiagarajan, K.N. Ramamurthy, P. Sattigeri, A. Spanias, Supervised local sparse coding of sub-image features for image retrieval, [in:] 2012 19th IEEE International Conference on Image Processing , pp. 3117-3120, 2012, doi: 10.1109/ICIP.2012.6467560.
• 27. A. Latif, A. Rasheed, U. Sajid, J. Ahmed, N. Ali, N.I. Ratyal, Z. Bushra, S.H. Dar, M. Sajid, T. Khalil, Content-based image retrieval and feature extraction: a comprehensive review, Mathematical Problems in Engineering , 2019, Article ID 9658350, 21 pages, 2019, doi: 10.1155/2019/9658350.
• 28. Y. Hou, Q. Wang, Research and improvement of content-based image retrieval framework, International Journal of Pattern Recognition and Artificial Intelligence , 32 (12), 1850043, 2018.
• 29. S. Jabeen, Z. Mehmood, T. Mahmood, T. Saba, A. Rehman, M.T. Mahmood, An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model, PloS ONE , 13 (4): e0194526, 2018, doi: 10.1371/journal.pone.0194526.
• 30. J.M. Medina, S. Jaime-Castillo, C.D. Barranco, J.R. Campana, On the use of a fuzzy object-relational database for flexible retrieval of medical images, IEEE Transactions on Fuzzy Systems , 20 (4): 786-803. 2012, doi: 10.1109/TFUZZ.2012.2201726.
• 31. E. Yildizer, A.M. Balci, T.N. Jarada, R. Alhajj, Integrating wavelets with clustering and indexing for effective content-based image retrieval, Knowledge-Based Systems , 31 : 55-66, 2012, doi: 10.1016/j.knosys.2012.01.013.
• 32. N. Jyothi, D. Madhavi, M.R. Patnaik, Optimization of log Gabor filters using genetic algorithm for query by image content systems, [in:] S. Choudhury, R. Mishra, R. Mishra, A. Kumar [Eds], Intelligent Communication, Control and Devices , pp. 799-806, Springer, Singapore, 2020.
• 33. M. Kokare, P.K. Biswas, B.N. Chatterji, Rotated complex wavelet based texture features for content based image retrieval, 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, August 23-26, pp. 652-655, 2004, doi: 10.1109/ ICPR.2004.1334250.
• 34. Q. Zheng, X. Tian, M. Yang, Y. Wu, H. Su, PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning, Multidimensional Systems and Signal Processing , 31 : 793-827, 2020, doi: 10.1007/s11045-019-00686-z.
• 35. Q. Zheng, M. Yang, X. Tian, N. Jiang, D. Wang, A full stage data augmentation method in deep convolutional neural network for natural image classification, Discrete Dynamics in Nature and Society , 2020 : Article ID 4706576, 11 pages, 2020, doi: 10.1155/2020/4706576.
• 36. Q. Zheng, P. Zhao, Y. Li, H. Wang, Y. Yang, Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification, Neural Computing and Applications , 33 : 7723-7745, 2021, doi: 10.1007/s00521-020-05514-1.
• 37. I. Loboda, Y. Feldshteyn, V. Ponomaryov, Neural networks for gas turbine fault identification: Multilayer perceptron or radial basis network?, International Journal of Turbo and Jet Engines , 29 (1): 37-48, 2012, doi: 10.1515/tjj-2012-0005.
• 38. Q. Liang, Y. Wang, W. Nie, Q. Li, MVCLN: multi-view convolutional LSTM network for cross-media 3D shape recognition, IEEE Access , 8 : 139792-139802, 2020, doi: 10.1109/AC-CESS.2020.3012692.
• 39. S. Antani, L.R. Long, G.R. Thoma, R.J. Stanley, Vertebra shape classification using MLP for content-based image retrieval, [in:] Proceedings of the International Joint Conference on Neural Networks , Vol. 1, pp. 160-165, 2003, doi: 10.1109/IJCNN.2003.1223324.
• 40. M. Kaur, D. Singh, Fusion of medical images using deep belief networks, Cluster Computing , 23 (2): 1439-1453, 2020, doi: 10.1007/s10586-019-02999-x.
• 41. S. Hussain, M. Hashmani, M. Moinuddin, M. Yoshida, H. Kanjo, Image retrieval based on color and texture feature using artificial neural network, [in:] B.S. Chowdhry, F.K. Shaikh, D.M.A. Hussain, M.A. Uqaili [Eds], Emerging Trends and Applications in Information Communication Technologies, IMTIC 2012, Communications in Computer and Information Science , vol. 281, pp. 501-511, Springer, Berlin, Heidelberg, 2012, doi: 10.1007/978-3-642-28962-0_47.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).