# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.2 Unsupervised  
In unsupervised techniques, clusters of frames are first created based on the quality of their content, and then the video summary is created by concatenating the keyframes of each cluster in chronological order. An unsupervised method, called FrameRank, was introduced by [PERSON] et al. [61]. They constructed a graph where frame similarity is measured at the edges and video frames correspond to vertices. To measure the relative importance of each segment and each video frame, they applied a graph-ranking technique. Depending on the type of summary, there are many ways in which the personalized summary process can be modeled, which are described hereafter.  
* _Contrastive learning:_ Using a pretext, self-supervised pretraining of a model can be performed, which is the approach to contrastive learning. According to contrastive learning, the model learns to repel representations intended to be far away, called negative representations, and to attract them from positive representations intended to be close to discriminate between different objects [56].
* _Reinforcement learning:_ A framework for creating an unsupervised personalized video summary that supports the integration of diverse pre-existing preferences, along with dynamic user interaction for selecting or omitting specific content types, was proposed by [PERSON] et al. [25]. Initially, the egocentric video captures spatio-temporal features using 3D convolutional neural networks (3D CNNs). Then, the video is split into non-overlapping frame shots, and the features are extracted. Subsequently, the features are imported by the reinforcement learning agent, which employs a bi-directional long- and short-term memory network (BiLSTM). Using forward and backward flow, BiLSTM serves to encapsulate future and past information from each subshot.  
* _Event-based keyframe selection (EKP)_: EKP was developed by [PERSON] et al. [71] so that keyframes can be presented in groups. The separation of groups is based on specific facts that are relevant to the query. The Multigraph Fusion (MGF) method is implemented to automatically find events that are relevant to the query. The keyframes in the different event categories are then separated from the correspondence between the videos and the keyframes. Through the two-level structure, the summarization is represented. Event descriptions are the first layer, and keyframes are the second layer.
* _Fuzzy rule based_: To represent human knowledge, which includes fuzziness and uncertainty, a method is a fuzzy system. From the theory of fuzzy sets, the fuzzy system is a representative and important application [26]. [PERSON] and [PERSON] [26] used a system based on fuzzy TSK rules. This system was used to evaluate video event shots. Also, in this rule-based system, consistency is a function of the variables used as input and not a linguistic variable, and therefore, the time-consuming decomposition process can be avoided. The summaries in [50; 51; 56; 59; 74] use an unsupervised technique to produce a summary.