hieve almost 10 times better accuracy in comparison to those that are based on high-level features.
2.3 Features
An area of heavy debate within video summarization and recommendation literature is the tradeoff between low-level features and high-level features, the former expressing semantic properties of media content that are obtained from meta-information (e.g., plot, genre, director, actors), and the latter being extracted directly from the media file itself, typically representing design aspects of a movie (such as lighting, colors, and motion). This tradeoff naturally forms a semantic gap problem that has been discussed heavily in the literature.
Much of the video summarization and recommendation literature is guided by the assumption that user preferences are influenced by high-level features to a greater extent than low-level features. For instance,
2.3.1 Low level features
Recent literature on RSs suggest that consumer preferences when choosing an item are influenced in a greater deal by visual aspects of items and less by their semantic features. Deldjoo, Elahi, Quadrana, and Cremonesi (2018) use low-level visual features extracted using the MPEG-7 standard and a deep neural network (DNN). The MPEG-7 standard extracts visual descriptors of images as color descriptors and texture descriptors. Alternatively, the authors used the activation values of inner neurons of the GoogLeNet DNN as visual features for each key frame. Whereas MPEG-7 features capture stylistic descriptors (i.e., color and texture), DNN features capture semantic content (e.g, objects, people, etc.). In this study, MPEG-7 features generated more accurate recommendations than semantic features (DNN). This could be due to the fact that while a DNN recognizes relevant semantic features (such as actors), it also recognizes non-relevant semantic features, which can create noise in the dataset.
Some studies have attempted to bridge the semantic gap by using both high-level and low-level features. For instance, Hermes and Schultz (2006) used face detection, cut detection, motion analysis, and text detection to be extracted automatically, and background information to be extracted from the Internet Movie Database (IMDb). Xu and Zhang (2013) use motion analysis
