# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Features
#### 4.2.3 Perception Based  
Perception-based video summaries refer to the ways, represented by high-level concepts, in which the content of a video can be perceived by the user. Some relevant examples include the association of the content of the video with some level of importance by the user, the size of the excitement that the user can have by watching the video, the amount that the user can perceive from the distraction videos, or the kind of emotions or the intensity that the user may perceive from the content. Other research areas apply theories such as semiotic theory, human perception theory, and user-attention theories as a means of interpreting the semantics of video content to achieve high-level abstraction. Therefore, unlike video summaries based on events and objects in which tangible events and objects present in the video content are identified, user perception-based summaries focus on extracting how the user perceives or can perceive the content of the video [11]. Hereafter, we present perception-based video summary methods.  
[PERSON] and [PERSON] [75] proposed a framework to generate a personalized video summarization that takes into account the user's evaluations of previously watched videos. The rating can be \"like\", \"neutral\", or \"dislike\". Scenes that affect the user of the video the most are collected, and their sequence forms the personalized video summary. A behavior pattern classifier presented by [PERSON] et al. [62] is based on 3D convolutional neural networks (3D CNNs) and uses visual evaluation features and 3D motion. The selection of elements is based on the degree of narrative, visual, and semantic perspective along with the user's preferences, as well as the user's attention behavior. [PERSON] et al. [53,54] proposed a video summarization system based on the intentions of the user according to a set of predefined idioms and expected duration. [PERSON] et al. [28] proposed a framework for summarizing movies based on people's emotional moments through facial expression recognition (FER). The emotions on which it is based are disgust, surprise, happy, sad, neutral, anger, and fear. [PERSON] and [PERSON] [32] proposed a human-centered video summary framework based on information derived from emotions and extracted from visual data. Initially, with the use of repetitive convolutional neural networks, emotional information is extracted, and then the emotional mechanisms of attention and information are expanded to enrich the video summary. [PERSON] et al. [22] presented a semi-automated gaze-based method of the eye for emotional video analysis. The behavioral signal received from the user and entered is pupil dilation (PD) to assess user engagement and arousal. In addition to discovering regions of interest (ROIs) and emotional video segments, the method includes fusion with content-based features and gaze analysis. The Interest Meter (IM) was proposed by [PERSON] et al. [27] to measure user interest through spontaneous reactions. By using a fuzzy fusion scheme, emotion and attention features are combined; this results in viewing behaviors being converted into quantitative interest scores, and a video summary is produced by combining those parts of the video that are considered interesting. [PERSON] and [PERSON] [72] proposed a framework for video summary based on the observer's behavior while monitoring the content of the video. The observer's behavior is detected on the basis of the operation of the video remote control and eye movement. [PERSON] and [PERSON] [40] proposed the DOI function to obtain a set of features of each video based on the interactive behavior of the viewers.