Special issue on deep learning in image and video retrieval
An overview of the state of the art of deep learning applied to video understanding is given by the paper “A Study on Deep Learning Spatiotemporal Models and Feature Extraction Techniques for Video Understanding” by M. Suresha, S. Kuppa and D.S. Raghukumar. The paper “Learning Visual Features for Re...
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| Veröffentlicht in: | International journal of multimedia information retrieval 2020-06, Vol.9 (2), p.61-62 |
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| container_title | International journal of multimedia information retrieval |
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| creator | Oerlemans, Ard Guo, Yanming Lew, Michael S. Chua, Tat-Seng |
| description | An overview of the state of the art of deep learning applied to video understanding is given by the paper “A Study on Deep Learning Spatiotemporal Models and Feature Extraction Techniques for Video Understanding” by M. Suresha, S. Kuppa and D.S. Raghukumar. The paper “Learning Visual Features for Relational CBIR” by N. Messina, G. Amato, F. Carrara, F. Falchi and C. Gennaro defines Relational Content-Based Image Retrieval as the task of finding images containing similar inter-object relationships. Evaluation on the ImageNet dataset and four well-known CNNs showed that their system achieves state-of-the-art performance. |
| doi_str_mv | 10.1007/s13735-020-00194-y |
| format | Article |
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| ispartof | International journal of multimedia information retrieval, 2020-06, Vol.9 (2), p.61-62 |
| issn | 2192-6611 2192-662X |
| language | eng |
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| subjects | Algorithms Classification Computer Science Data Mining and Knowledge Discovery Database Management Deep learning Editorial Feature extraction Image Processing and Computer Vision Image retrieval Information Storage and Retrieval Information Systems Applications (incl.Internet) Machine learning Multimedia Information Systems Retrieval State-of-the-art reviews |
| title | Special issue on deep learning in image and video retrieval |
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