Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval
DOI:
https://doi.org/10.31185/wjcms.147Keywords:
CNN, Deep Learning, Image Retrieval, SVMAbstract
The content-based image retrieval (CBIR) method operates on the low-level visual features of the user input query object, which makes it difficult for users to formulate the query and also does not provide adequate retrieval results. In the past, image annotation was suggested as the best possible framework for CBIR, which works on automatically signing keywords to images that support image retrieval. The recent successes of deep learning techniques, especially Convolutional Neural Networks (CNN), in solving computer vision applications have inspired me to work on this paper to solve the problem of CBIR using a dataset of annotated images
References
Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognit., vol. 40, no. 1, pp. 262–282, 2007.
Y. Liu, D. Zhang, and G. Lu, “Region-based image retrieval with high-level semantics using decision tree learning,” Pattern Recognit., vol. 41, no. 8, pp. 2554–2570, 2008.
F. Bastien et al., “Theano: new features and speed improvements,” ArXiv Prepr. ArXiv12115590, 2012.
A. Brock, S. De, S. L. Smith, and K. Simonyan, “High-performance large-scale image recognition without normalization,” in International Conference on Machine Learning, PMLR, 2021, pp. 1059–1071.
Y. Deng and B. S. Manjunath, “Unsupervised segmentation of color-texture regions in images and video,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 8, Art. no. 8, 2001.
Y. Han, F. Wu, Q. Tian, and Y. Zhuang, “Image annotation by input–output structural grouping sparsity,” IEEE Trans. Image Process., vol. 21, no. 6, pp. 3066–3079, 2012.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” ArXiv Prepr. ArXiv171204621, 2017.
C.-F. Tsai, “Bag-of-words representation in image annotation: A review,” Int. Sch. Res. Not., vol. 2012, 2012.
J. Wan et al., “Deep Learning for Content-Based Image Retrieval: A Comprehensive Study,” in Proceedings of the 22nd ACM international conference on Multimedia, Orlando Florida USA: ACM, Nov. 2014, pp. 157–166. doi: 10.1145/2647868.2654948.
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL: IEEE, Jun. 2009, pp. 248–255. doi: 10.1109/CVPR.2009.5206848.
E. H. Hssayni, N.-E. Joudar, and M. Ettaouil, “KRR-CNN: kernels redundancy reduction in convolutional neural networks,” Neural Comput. Appl., pp. 1–12, 2022.
S. Divya, B. Adepu, and P. Kamakshi, “Image Enhancement and Classification of CIFAR-10 Using Convolutional Neural Networks,” in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, 2022, pp. 1–7.
R. Doon, T. K. Rawat, and S. Gautam, “Cifar-10 classification using deep convolutional neural network,” in 2018 IEEE Punecon, IEEE, 2018, pp. 1–5.
D. Agrawal, A. Agarwal, and D. K. Sharma, “Content-Based Image Retrieval (CBIR): A Review,” Recent Innov. Comput. Proc. ICRIC 2021 Vol. 2, pp. 439–452, 2022.
J. Choe et al., “Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT,” Radiology, vol. 302, no. 1, pp. 187–197, 2022.
R. Vishraj, S. Gupta, and S. Singh, “A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions,” Comput. Electr. Eng., vol. 104, p. 108450, 2022.
M. Azimi Hemat, F. Shamsezat Ezat, and M. Kuchaki Rafsanjani, “Image Retrieval based on Multi-features using Fuzzy Set,” J. AI Data Min., vol. 10, no. 4, pp. 569–578, 2022.
X. Li, J. Yang, and J. Ma, “Recent developments of content-based image retrieval (CBIR),” Neurocomputing, vol. 452, pp. 675–689, 2021.
T. Ji, X. Liu, C. Deng, L. Huang, and B. Lang, “Query-adaptive hash code ranking for fast nearest neighbor search,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 1005–1008.
X. Liu, L. Huang, C. Deng, B. Lang, and D. Tao, “Query-adaptive hash code ranking for large-scale multi-view visual search,” IEEE Trans. Image Process., vol. 25, no. 10, pp. 4514–4524, 2016.
T. He, “Investigation of Animal Image Retrieval Algorithm Based on Deep Learning.,” Rev. Científica Fac. Cienc. Vet., vol. 29, no. 1, pp. 128–138, 2019.
E. S. S. H. Rizvi, “A New Framework to Bridge Semantic Gap for Semantics Based Image Retrieval,” Iqra University Islamabad Campus, 2014.
N. Kumar, P. Rani, V. Kumar, S. V. Athawale, and D. Koundal, “THWSN: Enhanced Energy-Efficient Clustering Approach for Three-Tier Heterogeneous Wireless Sensor Networks,” IEEE Sens. J., vol. 22, no. 20, Art. no. 20, 2022.
P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” Comput. Electr. Eng., vol. 105, p. 108543, Jan. 2023, doi: 10.1016/j.compeleceng.2022.108543.
G. Ansari, P. Rani, and V. Kumar, “A Novel Technique of Mixed Gas Identification Based on the Group Method of Data Handling (GMDH) on Time-Dependent MOX Gas Sensor Data,” in Proceedings of International Conference on Recent Trends in Computing, R. P. Mahapatra, S. K. Peddoju, S. Roy, and P. Parwekar, Eds., in Lecture Notes in Networks and Systems, vol. 600. Singapore: Springer Nature Singapore, 2023, pp. 641–654. doi: 10.1007/978-981-19-8825-7_55.
P. Rani, P. N. Singh, S. Verma, N. Ali, P. K. Shukla, and M. Alhassan, “An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment,” Wirel. Commun. Mob. Comput., vol. 2022, pp. 1–14, May 2022, doi: 10.1155/2022/3365392.
P. Rani, S. Verma, S. P. Yadav, B. K. Rai, M. S. Naruka, and D. Kumar, “Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System:,” Int. J. E-Health Med. Commun., vol. 13, no. 4, pp. 1–15, Sep. 2022, doi: 10.4018/IJEHMC.309436.
L. Liu, M. Yu, and L. Shao, “Learning short binary codes for large-scale image retrieval,” IEEE Trans. Image Process., vol. 26, no. 3, pp. 1289–1299, 2017.
X. Liu, J. He, and S.-F. Chang, “Hash bit selection for nearest neighbor search,” IEEE Trans. Image Process., vol. 26, no. 11, pp. 5367–5380, 2017.
H. Zhang, L. Liu, Y. Long, and L. Shao, “Unsupervised deep hashing with pseudo labels for scalable image retrieval,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 1626–1638, 2017.
R. Fu, B. Li, Y. Gao, and P. Wang, “Content-based image retrieval based on CNN and SVM,” in 2016 2nd IEEE International conference on computer and communications (ICCC), IEEE, 2016, pp. 638–642.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Rohit Sharma , Bipin Kumar Rai , Shubham Sharma
This work is licensed under a Creative Commons Attribution 4.0 International License.
