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Markatopoulou F, Moumtzidou A, Tzelepis C, Avgerinakis K, Gkalelis N, Vrochidis S, Mezaris V and Kompatsiaris I (2013), "ITI-CERTH participation to TRECVID 2013", In TRECVID 2013 Workshop, Gaithersburg, MD, USA. Gaithersburg, Maryland, USA, November, 2013. National Institute of Standards and Technology (NIST).
Abstract: This paper provides an overview of the tasks submitted to TRECVID 2013 by ITI-CERTH. ITICERTH participated in the Semantic Indexing (SIN), the Event Detection in Internet Multimedia (MED), the Multimedia Event Recounting (MER) and the Instance Search (INS) tasks. In the SIN task, techniques are developed, which combine new video representations (video tomographs) with existing well-performing descriptors such as SIFT, Bag-of-Words for shot representation, ensemble construction techniques and a multi-label learning method for score refinement. In the MED task, an efficient method that uses only static visual features as well as limited audio information is evaluated. In the MER sub-task of MED a discriminant analysis-based feature selection method is combined with a model vector approach for selecting the key semantic entities depicted in the video that best describe the detected event. Finally, the INS task is performed by employing VERGE, which is an interactive retrieval application combining retrieval functionalities in various modalities, used previously for supporting the Known Item Search (KIS) task.
BibTeX:
@inproceedings{tzelepis2013trecvid,
  author = { Markatopoulou, Fotini and Moumtzidou, Anastasia and Tzelepis, Christos and Avgerinakis, Kostas and Gkalelis, Nikolaos and Vrochidis, Stefanos and Mezaris, Vasileios and Kompatsiaris, Ioannis },
  title = {ITI-CERTH participation to TRECVID 2013},
  booktitle = {TRECVID 2013 Workshop, Gaithersburg, MD, USA},
  publisher = {National Institute of Standards and Technology (NIST)},
  year = {2013},
  url = {http://www-nlpir.nist.gov/projects/tvpubs/tv13.papers/iti-certh.pdf}
}
Tzelepis C, Gkalelis N, Mezaris V and Kompatsiaris I (2013), "Improving event detection using related videos and relevance degree support vector machines", In Proceedings of the 21st ACM international conference on Multimedia (MM 13). Barcelona, Catalunya, Spain, October, 2013. , pp. 673- -676. ACM.
Abstract: In this paper, a new method that exploits related videos for the problem of event detection is proposed, where related videos are videos that are closely but not fully associated with the event of interest. In particular, the Weighted Margin SVM formulation is modified so that related class observations can be effectively incorporated in the optimization problem. The resulting Relevance Degree SVM is especially useful in problems where only a limited number of training observations is provided, e.g., for the EK10Ex subtask of TRECVID MED, where only ten positive and ten related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2011 dataset verify the effectiveness of the proposed method.
BibTeX:
@inproceedings{tzelepis2013mm,
  author = {Tzelepis, Christos and Gkalelis, Nikolaos and Mezaris, Vasileios and Kompatsiaris, Ioannis},
  title = {Improving event detection using related videos and relevance degree support vector machines},
  booktitle = {Proceedings of the 21st ACM international conference on Multimedia (MM 13)},
  publisher = {ACM},
  year = {2013},
  pages = {673- -676},
  url = {http://dl.acm.org/citation.cfm?id=2502176},
  doi = {10.1145/2502081.2502176}
}
Tzelepis C, Mezaris V and Patras I (2016), "Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)", In MultiMedia Modeling - 22nd International Conference, MMM 2016, Miami, FL, USA, January 4-6, 2016, Proceedings, Part I. , pp. 3-15.
Abstract: In this paper, we propose an algorithm that learns from uncertain data and exploits related videos for the problem of event detection; related videos are those that are closely associated, though not fully depicting the event of interest. In particular, two extensions of the linear SVM with Gaussian Sample Uncertainty are presented, which a) lead to non-linear decision boundaries and b) incorporate related class samples in the optimization problem. The resulting learning methods are especially useful in problems where only a limited number of positive and related training observations are provided, e.g., for the $10$Ex subtask of TRECVID MED, where only ten positive and five related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED $2014$ dataset verify the effectiveness of the proposed methods.
BibTeX:
@inproceedings{tzelepis2016mmm,
  author = {Christos Tzelepis and Vasileios Mezaris and Ioannis Patras},
  title = {Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)},
  booktitle = {MultiMedia Modeling - 22nd International Conference, MMM 2016, Miami, FL, USA, January 4-6, 2016, Proceedings, Part I},
  year = {2016},
  pages = {3--15},
  url = {http://www.eecs.qmul.ac.uk/~ct300/pub/pdf/mmm16_preprint.pdf},
  doi = {10.1007/978-3-319-27671-7_1}
}