Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The LEMO annotation framework: weaving multimedia annotations with the web
International Journal on Digital Libraries
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Revising the wordnet domains hierarchy: semantics, coverage and balancing
MLR '04 Proceedings of the Workshop on Multilingual Linguistic Ressources
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Improved video categorization from text metadata and user comments
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Use what you have: Yovisto video search engine takes a semantic turn
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Improving video classification via youtube video co-watch data
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
NERD: a framework for unifying named entity recognition and disambiguation extraction tools
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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With the steady increase of videos published on media sharing platforms such as Dailymotion and YouTube, more and more efforts are spent to automatically annotate and organize these videos. In this paper, we propose a framework for classifying video items using both textual features such as named entities extracted from subtitles, and temporal features such as the duration of the media fragments where particular entities are spotted. We implement four automatic machine learning algorithms for multiclass classification problems, namely Logistic Regression (LG), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). We study the temporal distribution patterns of named entities extracted from 805 Dailymotion videos. The results show that the best performance using the entity distribution is obtained with KNN (overall accuracy of 46.58%) while the best performance using the temporal distribution of named entities for each type is obtained with SVM (overall accuracy of 43.60%). We conclude that this approach is promising for automatically classifying online videos.