Abstract

GUARDIA VACA, Denis Leandro  and  SANDOVAL ALCOCER, Juan Pablo. A sampling technique to categorize videos. RevActaNova. [online]. 2018, vol.8, n.4, pp.631-650. ISSN 1683-0789.

Automatically categorizing a large number of videos has many applications, for instance: security monitoring or video searching. Machine learning is one of the techniques that is used to automatically categorize videos. Beside the high precision of this technique, training a classification model may involve a large amount of time. This paper describes a sampling technique to reduce training time while keeping a reasonable precision. This technique uses machine learning to analyze only a sample of a video instead of its full content. To better understand the effect of the video sampling in the categorization, we analyzed a subset of videos from YouTube-8M, a video classification dataset with more than seven million of videos, we used two machine learning algorithms: logistic regression and recurrent neuronal networks. Our results show that depending of the sample size, the training time may be considerable reduced, slightly affecting the precision.

Keywords : video categorization; security systems; machine learning.

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