Open Access
| Issue |
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 4 | |
| Section | Image Processing | |
| DOI | https://doi.org/10.1051/matecconf/201925501003 | |
| Published online | 16 January 2019 | |
- SM. Jameel, MA. Hashmani, H. Alhussain, A. Budiman. A Fully Adaptive Image Classification Approach for Industrial Revolution 4.0. IRICT 2018. pp. 311–321. 2018. [Google Scholar]
- Li, Jia, and Z. James. Real-time computerized annotation of pictures. Proceedings of the 14th ACM international conference on Multimedia. ACM, 2006. [Google Scholar]
- Tamura, Hideyuki, and Y. Naokazu. Image database systems: A survey. Pattern recognition 17.1. pp. 29–43 (1984). [CrossRef] [Google Scholar]
- Chang, S-K., and H. Arding. Image information systems: where do we go from here?. IEEE transactions on Knowledge and Data Engineering 4.5, pp. 431–442 (1992). [CrossRef] [Google Scholar]
- N. Rasiwasia, N. Vasconcelos, P.J. Moreno. Query by semantic example. Proceedings of the Fifth International Conference on Image and Video Retrieval, 4071, pp. 51–60 (2006). [CrossRef] [Google Scholar]
- N. Vasconcelos. Minimum probability of error image retrieval. IEEE Transactions on Signal Processing 52 (8), pp. 2322–2336 (2004). [CrossRef] [Google Scholar]
- Uricchio, Tiberio. Automatic image annotation via label transfer in the semantic space Pattern Recognition 71, pp. 144–157(2017). [Google Scholar]
- Zhang, Pengyu. Automatic Image Annotation Based on Multi-Auxiliary Information. IEEE Access 5, pp. 18402–18411 (2017). [CrossRef] [Google Scholar]
- Pobar, Miran, and I Marina. Automatic image annotation refinement. MIPRO, 2016 39th International Convention on. IEEE (2016). [Google Scholar]
- Hao, Zhangang, Ge. Hongwei, Gu. Tianpeng. Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering. Mathematical Problems in Engineering (2017). [Google Scholar]
- Liu, Ying. A survey of content-based image retrieval with high-level semantics.” Pattern recognition 40.1, pp. 262–282 (2007). [CrossRef] [Google Scholar]
- Lew, S. Michael. Content-based multimedia information retrieval: State of the art and challenges. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2.1, pp. 1–19 (2006). [Google Scholar]
- Vasconcelos, Nuno. From pixels to semantic spaces: Advances in content-based image retrieval. Computer 40.7 (2007). [CrossRef] [Google Scholar]
- Datta, Ritendra. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (Csur) 40.2, pp. 5 (2008). [CrossRef] [Google Scholar]
- Cheng, Qimin. A survey and analysis on automatic image annotation. Pattern Recognition 79, pp. 242–259 (2018). [CrossRef] [Google Scholar]
- Srivastava, Gargi, R. Srivastava. A Survey on Automatic Image Captioning. International Conference on Mathematics and Computing. Springer, Singapore. (2018). [Google Scholar]
- MA. Hashmani, SM. Jameel. An Ensemble Approach to Big Data Security (Cyber Security) (IJACSA), 9.9 (2018). http://dx.doi.org/10.14569/IJACSA.2018.090910. [Google Scholar]
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