| Tài liệu tham khảo |
Loại |
Chi tiết |
| [1] C. Bobed, R. Yus, F. Bobillo, S. Ilarri, J. Bernad, E. Mena, R. Trillo-Lado, Á.L.Garrido, "Emerging Semantic-Based Applications", Semantic Web, Cham, pp.39- 83, 2016 |
Sách, tạp chí |
| Tiêu đề: |
Emerging Semantic-Based Applications |
|
| [3] Y. Cao, M. Long, J. Wang, Q. Yang, P.S. Yu, "Deep Visual-Semantic Hashing for Cross-Modal Retrieval", Proceedings of the 22nd ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp.1445-1454, 2016 |
Sách, tạp chí |
| Tiêu đề: |
Deep Visual-Semantic Hashing forCross-Modal Retrieval |
|
| [4] Y. Chou, D.J. Lee, D. Zhang, "Semantic-Based Brain MRI Image Segmentation Using Convolutional Neural Network", ISVC: Advances in Visual Computing, Cham, pp.628-638, 2016 |
Sách, tạp chí |
| Tiêu đề: |
Semantic-Based Brain MRI Image SegmentationUsing Convolutional Neural Network |
|
| [5] L. Deligiannidis, H.R. Arabnia, "Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition", ed S. Elliot, Elsevier, USA: Morgan Kaufmann, Waltham, MA 02451, 2015 |
Sách, tạp chí |
| Tiêu đề: |
Emerging Trends in Image Processing, ComputerVision, and Pattern Recognition |
|
| [6] Deloitte, "Photo sharing: trillions and rising", Deloitte Touche Tohmatsu Limited, Deloitte Global, 2016 |
Sách, tạp chí |
| Tiêu đề: |
Photo sharing: trillions and rising |
|
| [8] J. Gantz, D. Reinsel, "THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digi tal Shadows, and Biggest Growth in the Far East", IDC iView, EMC Corporation, 2012 and 2014 |
Sách, tạp chí |
| Tiêu đề: |
THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East |
|
| [9] C. Kurtz, A. Depeursinge, C.F. Beaulieu, "A semantic framework for the retrieval of similar radiological images based on medical annotations", IEEE International Conference on Image Processing (ICIP), Paris, France, 2014 |
Sách, tạp chí |
| Tiêu đề: |
A semantic framework for the retrievalof similar radiological images based on medical annotations |
|
| [10] P. Muneesawang, N. Zhang, L. Guan, "Multimedia Database Retrieval: Technology and Applications", Springer, New York Dordrecht London, 2014 |
Sách, tạp chí |
| Tiêu đề: |
Multimedia Database Retrieval: Technology and Applications |
|
| [11] A.B. Spanier, D. Cohen, L. Joskowicz, "A new method for the automatic retrieval of medical cases based on the RadLex ontology", International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 3, pp.471–484, 2017 |
Sách, tạp chí |
| Tiêu đề: |
A new method for the automatic retrievalof medical cases based on the RadLex ontology |
|
| [12] V. Vijayarajan, M. Dinakaran, P. Tejaswin, M. Lohani, "A generic framework for ontology-based information retrieval and image retrieval in web data", Human- centric Computing and Information Sciences, vol. 6, no. 18, pp.1-30, 2016 |
Sách, tạp chí |
| Tiêu đề: |
A generic framework forontology-based information retrieval and image retrieval in web data |
|
| [13] X. Xie, X. Cai, J. Zhou, N. Cao, Y. Wu, "A Semantic-based Method for Visualizing Large Image Collections", IEEE Transactions on Visualization and Computer Graphics, IEEE Computer Society, vol. xx, no. xx, pp.xx-xx, 2018 |
Sách, tạp chí |
| Tiêu đề: |
A Semantic-based Method forVisualizing Large Image Collections |
|
| [14] [14] B.B.Z. Yao, X. Yang, L. Lin, M.W. Lee, S.-C. Zhu, "I2T: Image Parsing to Text Description", Proceedings of the IEEE, pp.1485-1508, 2010 |
Sách, tạp chí |
| Tiêu đề: |
I2T: Image Parsing to Text Description |
|
| [15] Li, Wen, et al. "Text-based image retrieval using progressive multi-instance learning." 2011 International Conference on Computer Vision. IEEE, 2011 |
Sách, tạp chí |
| Tiêu đề: |
Text-based image retrieval using progressive multi-instance learning |
|
| [16] Tollari, Sabrina, et al. "Exploiting visual concepts to improve text-based image retrieval." European Conference on Information Retrieval. Springer, Berlin, Heidelberg, 2009 |
Sách, tạp chí |
| Tiêu đề: |
Exploiting visual concepts to improve text-based imageretrieval |
|
| [17] Karthikeyan, T., P. Manikandaprabhu, and S. Nithya. "A survey on text and content-based image retrieval system for image mining." International Journal of Engineering 3 (2014) |
Sách, tạp chí |
| Tiêu đề: |
A survey on text andcontent-based image retrieval system for image mining |
|
| [18] Al-Mohamade, Abeer, Ouiem Bchir, and Mohamed Maher Ben Ismail. "Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning."Journal of Imaging 6.1 (2020): 2 |
Sách, tạp chí |
| Tiêu đề: |
MultipleQuery Content-Based Image Retrieval Using Relevance Feature Weight Learning |
| Tác giả: |
Al-Mohamade, Abeer, Ouiem Bchir, and Mohamed Maher Ben Ismail. "Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning."Journal of Imaging 6.1 |
| Năm: |
2020 |
|
| [19] Douik, Ali, Mehrez Abdellaoui, and Leila Kabbai. "Content based image retrieval using local and global features descriptor." 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2016 |
Sách, tạp chí |
| Tiêu đề: |
Content based image retrievalusing local and global features descriptor |
|
| [21] Chandresh Pratap Singh, "R-Tree implementation of image databases ". Signal & Image Processing: An International Journal (SIPIJ) Vol.2, No.4, December 2011 |
Sách, tạp chí |
| Tiêu đề: |
R-Tree implementation of image databases |
|
| [22] Shifeng Wu1, Huazhu Song, Gui Cheng1, Xian Zhong, “Civil engineering supervision video retrieval method optimization based on spectral clustering and R- tree”. The Natural Computing Applications Forum, 2018 |
Sách, tạp chí |
| Tiêu đề: |
Civil engineeringsupervision video retrieval method optimization based on spectral clustering and R-tree |
|
| [23] Xia Tiantian, Lin Hong, Li Yuqiang, “Spatio-temporal Trajectory Gatherings Pattern Mining Method Based on R* tree Index”. 3rd International Conference for Convergence in Technology (I2CT), 2018 |
Sách, tạp chí |
| Tiêu đề: |
Spatio-temporal Trajectory GatheringsPattern Mining Method Based on R* tree Index |
|