# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 5 Types of Methodology
### Personalized Video Summarization Using Clustering  
The text method proposed by [PERSON] et al. [74] first extracts the nouns from the input text and then the videos are clustered into groups based on each event after being segmented. The priority of each section is calculated according to the clusters that have been created. Segments that are high priority have a higher chance of being included in the video summary. The video summary is then generated by selecting the subset of segments that is optimal according to the calculated priority calculation.  
Based on clustering, the categories into which the personalized video summary can be divided are hierarchical clustering, aggregation hierarchical clustering, hierarchical context clustering, k-means clustering, dense-neighbor-based clustering, concept clustering and affinity propagation.  
* _Hierarchical clustering:_ Hierarchical clustering, or hierarchical cluster analysis, is a method that aggregates similar items into collections known as clusters. The final result is a series of clusters, each unique from the others, and the elements within each cluster share a high degree of similarity. [PERSON] et al. [51] proposed a hierarchy to encode visual features. The hierarchy will be used as a dictionary to encode visual features in an improved way. Initially, a cluster of leaf nodes is created hierarchically based on their paired similarities. The same process is then retrospectively performed to cluster the images into subgroups. The result of this process is the creation of a semantic tree from the root to the leaves.
* _K-means clustering:_ The Non-negative Matrix Factorization (NMF) method was proposed by [PERSON] et al. [63] to produce supervised personalized video summarization. The video is segmented into small clips, and each clip is described by a word from the bag-of-words model. The NMF method is used for action segmentation and clustering. Using the k-means algorithm, the dictionary is learned. The unsupervised method proposed by [PERSON] et al. [71] uses a k-means algorithm to cluster words containing similar meanings. In the method proposed by [PERSON] et al. [70], clustering is performed using the k-means algorithm. From the frames, the extraction of visual characteristics is carried out using CNN, which is pre-trained. Each shot is represented by the average of visual characteristics. With the k-means method, the shots are clustered into a set of clusters. The centers are then initialized using the Euclidean distance metric by taking random samples from the data. When the shots closest to each center of the cluster areselected, the summary is created. To cluster photos, [PERSON] and [PERSON] [7] used the K-means algorithm in their supervised framework to categorize them based on RGB color histograms.
* _Dense-neighbor based clustering:_ [PERSON] et al. [61] proposed an unsupervised framework to produce a personalized video summary. With a clustering method, the video is divided into separate segments. The method is based on dense neighbors. They clustered the video frames into discrete segments that are semantically consistent frames using a clustering center clustering algorithm.
* _Concept clustering:_ Sets of video frames that are different but semantically close to each other are grouped together. Clusters of concepts are formed from this grouping. For the representation of macro-optical concepts, which are created from similar types of micro-optical concepts of video units that are different, these clusters are intended. Using the cumulative clustering technique, sets of frames are clustered. The summation technique is top-down and is based on the pairwise similarity function (SimFS) operating on two sets of frames [8].
* _Affinity propagation:_ It is a clustering approach that has a higher fitness value than other approaches. AP is based on factor graphs and has been used as a tool to create storyboard summaries and cluster video frames [15].