# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.1 Supervised  
In supervised techniques, a model is first trained using labeled data, and then the video summary is produced based on that model. Hereafter, we present supervised approaches. Deep learning for player detection was proposed by [PERSON] and [PERSON] [44]. To address the multiscale matching problem in the person search, [PERSON] et al. [29] used the Cross-Level Semantic Alignment (CLSA) model. From the identity features, the most discriminative representations are learned using the end-to-end CLSA model. The Deep-SORT algorithm was used by [PERSON] et al. [73] to track objects. [PERSON] and [PERSON] [21] proposed a deep learning method to generate query-based video summarization for a visual text embedding space. The method is end-to-end and consists of a video digest generator, a video digest controller, and a video digest output module. OoI detection was performed using YOLOv3 by [PERSON] et al. [33]. A single neural network was applied to the full video. The frames are divided into regions, delineated, and the neural network predicts probabilities. A deep architecture was trained by [PERSON] et al. [57] to perform efficient learning of the semantic embeddings of video frames. The learning is conducted through the progressive exploitation of the data from the captions of the images. According to the implemented algorithm, the semantically relevant segments of the original video stream are selected according to the context of the sentence or text description provided by the user. Studies [5,7,13,14,19,24,27,32,33,36,44,48,60,62] used the supervised technique to produce a summary. Active Video Summary (AVS) was suggested by [PERSON] et al. [68], which constantly asks the user questions to obtain the user's preferences about the video, updating the summary online. From each extracted frame, object recognition is performed using a neural network. Using a line search algorithm, the summary is generated. [PERSON] et al. [30] introduced a video summarization framework that employs a deep ranking model to analyze large-scale collections of Flickr images. This approach identifies user interests to autonomously create significant video summaries. This framework does not simply use hand-crafted features to calculate the similarity between a set of web images and a frame. [PERSON] et al. [45] proposed a method that uses two separate neural networks to transform the joint positions of the human body and the RGB frames of the video stream as input types. To identify the highlights, the two streams are merged into a single representation. The network is trained using the UGSV dataset from the lower to the upper layers. Depending on the type of summary, there are many ways that the personalized summary process can be modeled. * _Keyframe selection:_ The goal is to identify the most inclusive and varied content (or frames) in a video for brief summaries. Keyframes are used to represent significant information included in the video [52]. The keyframe-based approach chooses a limited set of image sequences from the original video to provide an approximate visual representation [31]. [PERSON] et al. [52] proposed a method in which user preference is entered as an image query. The method is based on object recognition with automatic keyframe recognition. From the input video, the important frame is selected, so that the output video is produced from these frames. Based on the similarity score between the input video and the query image, a keyframe is selected. A selection table is created from the keyframe that is decided to be selected. A threshold is applied to the selection score. If the frame has a selection score greater than the threshold value, then this frame is a keyframe; otherwise, the frame is not considered a keyframe and is discarded. * _Keyshot selection:_ The keyshots comprise standard continuous video segments extracted from full-length video, each of which is shorter than the original video.