A Content-Based Image Retrieval System Using Semi-Supervised Learning and Frequent Patterns Mining
Subject Areas : electrical and computer engineering
1 - Islamic Azad University,
Keywords: Wavelet transform, image recommender, frequent patterns mining, machine learning,
Abstract :
Content-based image retrieval, which is also known as query based on image content, is one of the sub-branches of machine vision, which is used to organize and recognize the content of digital images using visual features. This technology automatically searches the images similar to the query image from huge image database and it provides the most similar images to the users by directly extracting visual features from image data; not keywords and textual annotations. Therefore, in this paper, a method is proposed that utilizes wavelet transformation and combining features with color histogram to reduce the semantic gap between low-level visual features and high-level meanings of images. In this regard, the final output will be presented using the feature extraction method from the input images. In the next step, when the query images are given to the system by the target user, the most similar images are retrieved by using semi-supervised learning that results from the combination of clustering and classification based on frequent patterns mining. The experimental results show that the proposed system has provided the highest level of effectiveness compared to other methods.
[1] Y. Zheng and D. X. Wang, "A survey of recommender systems with multi-objective optimization," Neurocomputing, vol. 474, pp. 141-153, Feb. 2022.
[2] A. Ortigosa, R. M. Carro, and J. I. Quiroga, "Predicting user personality by mining social interactions in Facebook," J. of Computer and System Sciences, vol. 80, no. 1, pp. 57-71, Feb. 2014.
[3] C. C. Aggarwal, Recommender Systems, 1st Ed., p 518, Springer International Publishing, 2016.
[4] M. Kolahkaj, A. Harounabadi, A. Nikravanshalmani, and R. Chinipardaz, "A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining," Electronic Commerce Research and Applications, vol. 42, Article ID: 100978, Jul./Aug. 2020.
[5] R. Katarya and O. P. Verma, "Recommender system with grey wolf optimizer and FCM," Neural Comput & Applic, vol. 30, no. 5, pp. 1679-1687, Sept. 2018.
[6] M. Kolahkaj, A. Harounabadi, A. Nikravanshalmani, and R. Chinipardaz, "DBCACF: a multidimensional method for tourist recommendation based on users' demographic, context and feedback," Information Systems and Telecommunication, vol. 4, no. 6, pp. 209-219, Autumn 2019.
[7] M. Kolahkaj, A. Harounabadi, A. Nikravanshalmani, and R. Chinipardaz, "Incorporating multidimensional information into dynamic recommendation process to cope with cold start and data sparsity problems," J. of Ambient Intelligence and Humanized Computing, vol. 12, no. pp. 9535-9554, Oct. 2021.
[8] N. Kayhan and S. Fekri-Ershad, "Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns," Multimedia Tools and Applications, vol. 80, no. 21, pp. 32763-32790, 2021.
[9] P. Srivastava and A. Khare, "Content-based image retrieval using local ternary wavelet gradient pattern," Multimed Tools Appl, vol. 78, no. 24, pp. 34297-34322, 2019.
[10] N. Ghosh, S. Agrawal, and M. Motwani, "A survey of feature extraction for content-based image retrieval system," in Proc. of Int. Conf. on Recent Advancement on Computer and Communication, pp. 305-313, Singapore, Apr. 2018.
[11] A. Du, L. Wang, and J. Qin, "Image retrieval based on colour and improved NMI texture features," Automatika, vol. 60, no. 4, pp. 491-499, 2019.
[12] A. Irtaza, M. Arfan Jaffar, E. Aleisa, and T. S. Choi, "Embedding neural networks for semantic association in content based image retrieval," Multimed Tools Appl, vol. 72, pp. 1911-1931, Sept. 2013.
[13] M. Garg and G. Dhiman, "A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants," Neural Computing and Applications, vol. 33, no. 4, pp. 1311-1328, Fex. 2021.
[14] F. Rajam and S. Valli, "A survey on content based image retrieval," Life Science J., vol. 10, no. 2, pp. 2475-2487, 2013.
[15] A. Mishra, M. H. Khan, W. Khan, M. Zunnun Khan, and N. Kumar Srivastava, "A comparative study on data mining approach using machine learning techniques: prediction perspective," Part of the EAI/Springer Innovations in Communication and Computing Book Series (EAISICC), pp. 153-165, 2021.
[16] I. Viktoratos, A. Tsadiras, and N. Bassiliades, "Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems," Expert Systems with Applications, vol. 101, pp. 78-90, Jul. 2018.
[17] M. V. Ahluwalia, A. Gangopadhyay, and Z. Chen, "Target-based, privacy preserving, and incremental association rule mining," IEEE Trans. on Services Computing, vol. 10, no. 4, pp. 633-645, Jul./Aug. 2017.
[18] A. K. Singh, A. Kumar, and A. K. Maurya, "An empirical analysis and comparison of apriori and fp-growth algorithm for frequent pattern mining," in Proc. of IEEE Int. Conf. on Advanced Communication Control and Computing Technologies, pp. 1599-1602, Ramanathapuram, India, 8-12 May 2014.
[19] H. Cheng, X. Yan, J. Han, and P. S. Yu, "Direct discriminative pattern mining for effective classification," in Proc. of IEEE Int. Conf. on on Data Engineering, ICDE'08, pp. 169-178, Cancun, Mexico, 7-12 Apr. 2008.
[20] Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, "A review on deep learning for recommender systems: challenges and remedies," Artif. Intell. Rev., vol. 52, pp. 1-37, Jun. 2019.
[21] Z. Yu, H. Xu, Z. Yang, and B. Guo, "Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints," IEEE Trans. on Human-Machine Systems, vol. 46, no. 1, pp. 151-158, Feb. 2016.
[22] M. J. Zaki, "SPADE: an efficient algorithm for mining frequent sequences," Machine Learning, vol. 42, pp. 31-60, 2001.
[23] M. J. Zaki, "An efficient algorithm for mining frequent sequences," Mach. Learn., vol. 42, pp. 31-60, Jan. 2000.
[24] M. Sinthuja, D. Evangeline, S. P. Raja, and G. Shanmugarathinam, "Frequent itemset mining algorithms-a literature survey," Intelligent Sustainable Systems, vol. 213, pp. 159-166, Aug. 2022.
[25] P. Parvathi Sangeetha and S. Hemamalini, "Rational-dilation wavelet transform based torque estimation from acoustic signals for fault diagnosis in a three phase induction motor," IEEE Trans. on Industrial Informatics, vol. 15, no. 6, pp. 3492-3501, Jun. 2019.
[26] M. Kolahkaj and M. Khalilian, "A recommender system by using classification based on frequent pattern mining and J48 algorithm," in Proc. of 2nd. Int. Conf. on Knowledge-Based Engineering and Innovation, pp. 405-411, Tehran, Iran, 5-6 Nov. 2015.
[27] M. Kolahkaj, A. Haroun Abadi, and M. Sadegh Zadeh, "A recommender system for web mining using neural network and fuzzy algorithm," International J. of Computer Applications, vol. 78, no. 8, pp. 20-24, Sep. 2013.
[28] A. K. Bhunia, A. Bhattacharyya, P. Banerjee, P. P. Roy, and S. Murala, "A novel feature descriptor for imageretrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern," Pattern Anal Applic, vol. 23, pp. 1-21, May 2019.