# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work
### Research Gap  
A review of the current state-of-the-art shows several initiatives in investigating consumer engagement in advertising. However, these efforts are still mainly aimed at conceptualizing engagement towards a unified theory, while operationalisations of engagement into valid psychometric instruments are still relatively rare. Table 1 provides an overview of the different approaches. To our knowledge, there are currently only two standardized instruments for measuring consumer engagement and providing ground truth: Consumer Brand Engagement Scale (CBS) [8] and User Engagement Scale (UES) [5]. The CBS focuses on brand awareness as a particular aspect of consumer engagement, while the UES is more general and robust. Of the applied approaches presented (cf. [9; 13; 15; 17; 18]), none uses any of these tools in their modeling, instead relying on combinations of engagement indicators, such as the number of views, the like/dislike ratio, or the sentiment of comments.  
Moreover, as can be seen from Table 2, there are no engagement studies based on computable low-level features of the advertising video materials \(v\). The table shows applications of low-level video features relevant to the presented research. MPEG-7 Visual, for example, uses multidimensional features that have proven useful in multimedia indexing  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Task** & **Method** & **Findings** \\ \hline Video indexing and retrieval [22]. & Use MPEG-7 Visual features to search and retrieve videos based on distance measures in feature space. & Performance depends on specific case. Other distance measures may be required. \\ \hline Statistical analysis of MPEG-7 descriptors [23]. & Mean and variance of description elements, distribution of elements, cluster analysis (hierarchical and topological) and factor analysis were performed. & The analysis revealed that most MPEG-7 descriptions are highly redundant and sensitive. \\ \hline Movie recommendation & Deep-learning based recommender system based on MPEG-7 visual features [27]. & A working proof of concept demonstrates a potential of computable visual features for recommender systems. A large dataset is required for deep neural net training. \\ \hline Applied media aesthetics [24]. & Computational evaluation of production techniques on viewers’ aesthetic reflexes based on the aesthetic elements of multimedia items. & Fundamental elements of media aesthetics can be used to evaluate the effective visual appeal of images and videos. \\ \hline Content-based video recommendation system based on stylistic visual features [26]. & Recommender system based on low-level video features defined upon applied media aesthetic elements as a substitute to semantic media metadata. & Compared with semantic content-based recommender systems, the proposed method leads to more accurate results. \\ \hline The use of computable video classification [28] & Classification of movie genres based on computable video features. & Average Shot Length, Color variance, Lighting Key, and Motion Content features can be used as classifiers of movie genre. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contributions of related work regarding the applications of computable low-level video features.  
and retrieval [22]. Given the high dimensionality and redundancies found in MPEG-7 descriptors [23], a much larger dataset is required for a modeling attempt using deep learning techniques. Other research shows that visual features related to applied media aesthetics can behave well as media classifiers even for feature vectors with low dimensionality.