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Presenting a method based on automatic image annotation techniques for the semantic recovery of images using artificial neural networks | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 3، دوره 14، شماره 11، بهمن 2023، صفحه 25-39 اصل مقاله (1.57 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.29216.4088 | ||
| نویسنده | ||
| Bita Amirshahi* | ||
| Department of Computer Engineering and Information Tehnology, Payame Noor University, Tehran, Iran | ||
| تاریخ دریافت: 12 آبان 1401، تاریخ بازنگری: 15 آذر 1401، تاریخ پذیرش: 11 دی 1401 | ||
| چکیده | ||
| With the ever-increasing growth of the Internet and the digital imaging industry, the need to organize and separate images is strongly felt. As a result, image databases with very large sizes were created. In such a situation, there is a strong need for effective tools and methods for image search and recovery. In this proposed method, an automatic image delineation method using an artificial neural network has been presented. At first, by studying reference books and articles related to the basic concepts that are necessary to start working in this field, then by studying the articles of recent years in the desired field, we will examine the strengths and weaknesses of the subject in more detail. Finally, by implementing and testing various ideas that have been expressed in the articles, and with the guidance of the supervisor and applying his ideas, he has tried to cover the weaknesses of other ideas, and finally, a method to improve the accuracy of image indexing and retrieval methods in databases based on the content of images to achieve high efficiency and reduce the semantic gap between low-level features and human perceptual concepts. The proposed color extraction method is simpler, less computationally complex, more accurate and faster. The results of evaluations and comparisons indicate the relative superiority of the proposed method over other methods. | ||
| کلیدواژهها | ||
| Automatic image؛ Annotation techniques؛ Semantic recovery؛ Artificial neural networks | ||
| مراجع | ||
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[1] M.M. Adnan, M.S. M. Rahim, A. Rehman, Z. Mehmood, T. Saba and R.A. Naqvi, Automatic image annotation based on deep learning models: A systematic review and future challenges, IEEE Access 9 (2021), 50253–50264. [2] A. Bahrololoum and H. Nezamabadi-pour, A multi-expert based framework for automatic image annotation, Pattern Recogn. 61 (2017), 169–184. [3] K.A. Kadhim, F. Mohamed and Z.N. Khudhair, Deep learning: Classification and automated detection earlier of Alzheimer’s disease using brain MRI images, J. Phys. Conf. Ser. 1892 (2021), no. 1. [4] X. Ke, Data equilibrium based automatic image annotation by fusing deep model and semantic propagation, Pattern Recogn. 71 (2017), 60–77. [5] L. Li, Sh. Tang, L. Deng, Y. Zhang and Q. Tian, Image caption with global-Local attention, Thirty-First AAAI Conf. Artific. Intel. (AAAI-17), 2017. [6] J. Liu and W. Wu, Automatic image annotation using improved Wasserstein generative adversarial networks, IAENG Int. J. Comput. Sci. 48 (2021), no. 3, 17. [7] R. Rad and M. Jamzad, Image annotation using multi-view non-negative matrix factorization with different number of basis vectors, J. Visual Commun. Image Represent. 46 (2017), 1–12. [8] D. Ramachandram and G.W. Taylor, Deep multimodal learning: A survey on recent advances and trends, Signal Process. Mag. IEEE 34 (2017), 6–108. [9] J. Sandra, J. Antonio, C. Mora and A. Almeida, A novel trademark image retrieval system based on multi-feature extraction and deep networks, J. Imaging. 8(9) (2022). [10] T. Sun, L. Sun and D.Y. Yeung, Fine-grained categorization via CNN-based automatic extraction and integration of object-level and part-level features, Image Vision Comput. 64 (2017), 47–66. [11] L. Songhao, Sparse multi-modal topical coding for image annotation, Neurocomput. 214 (2016), 162–174. [12] L. Tsochatzidis, Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach, Pattern Recogn. 71 (2017), 106–117. [13] Zh. Qian, P. Zhong and J. Chen, Integrating global and local visual features with semantic hierarchies for two-level image annotation, Neurocomput. 171 (2016), 1167–1174. [14] D. Tian and Z. Shi, Automatic image annotation based on Gaussian mixture model considering cross-modal correlations, J. Visual Commun. Image Represent. 44 (2017), 50–60. [15] Ch. Tsai, Bag-of-words representation in image annotation: A review, Int. Scholar. Res. Notices 2012 (2012). [16] T. Uricchio, Automatic image annotation via label transfer in the semantic space, Pattern Recogn. 71 (2017), 144–157. [17] A. Vatani, M. Taleby Ahvanooey and M. Rahimi, Presenting a method based on automatic image annotation techniques for the semantic recovery of images using artificial neural networks, Int. J. Adv. Comput. Sci. Appl. 9 (2018), no. 3. [18] R. Wang, Y. Xie, J. Yang, L. Xue, M. Hu and Q. Zhang, Large scale automatic image annotation based on convolutional neural network, J. Vis. Commun. Image R. 49 (2017), 213–224. [19] M. Wang, Effective automatic image annotation via integrated discriminative and generative models, Inf. Sci. 262 (2014), 159–171. [20] J. Zhang, Y. Zhao, D. Li, Zh. Chen and Y. Yuan, A novel image annotation model based on content representation with multi-layer segmentation, Neural Comput. Appl. 26 (2015), no. 6, 1407–1422. [21] S. Zhu, X.Sun and D. Jin, Multi-view semi-supervised learning for image classification, Neurocomput. 208 (2016), 136–142. | ||
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