In this paper, we present a computational model capable to predict the viewer perception of car advertisements videos by using a set of low-level video descriptors. Our research goal relies on the hypothesis that these descriptors could reflect the aesthetic value of the videos and, in turn, their viewers' perception. To that effect, and as a novel approach to this problem, we automatically annotate our video corpus, downloaded from YouTube, by applying an unsupervised clustering algorithm to the retrieved metadata linked to the viewers' assessments of the videos. In this regard, a regular k-means algorithm is applied as partitioning method with k ranging from 2 to 5 clusters, modeling different satisfaction levels or classes. On the other hand, available metadata is categorized into two different types based on the profile of the viewers of the videos: metadata based on explicit and implicit opinion respectively. These two types of metadata are first individually tested and then combined together resulting in three different models or strategies that are thoroughly analyzed. Typical feature selection techniques are used over the implemented video descriptors as a pre-processing step in the classification of viewer perception, where several different classifiers have been considered as part of the experimental setup. Evaluation results show that the proposed video descriptors are clearly indicative of the subjective perception of viewers regardless of the implemented strategy and the number of classes considered. The strategy based on explicit opinion metadata clearly outperforms the implicit one in terms of classification accuracy. Finally, the combined approach slightly improves the explicit, achieving a top accuracy of 72.18% when distinguishing between 2 classes, and suggesting that better classification results could be obtained by using suitable metrics to model perception derived from all available metadata.
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