# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Source of Information
#### 4.4.1 Internal Personalized Summarization Techniques  
Internal personalized summarization techniques are the techniques that analyze and use information that is internal to the content of the video stream. During the second stage of the video life cycle and, more specifically, for the video content produced, internal summarization techniques apply. Through these techniques, low-level text, audio, and image features are automatically parsed from the video stream into abstract semantics suitable for video summarization [11].  
* _Image features_ may have changes in the motion, shape, texture, and color of objects that come from the video image stream. These changes can be used to perform video segmentation in shots by identifying fades or hot boundaries. Fades are identified  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Domain** & **Papers** \\ \hline Cultural heritage & [62] \\ \hline Movies/serial clips & [19; 28; 29; 36; 50] \\ \hline Office videos & [26] \\ \hline Personal videos & [53; 54] \\ \hline Sports video & [18; 36; 38; 39; 40; 41; 42; 43; 44; 45] \\ \hline Zoo videos & [8] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of domains.  
Figure 7: Distribution of the papers reported in Table 1 in the domain of personalized summary.  
by slow changes in the characteristics of an image. Hot boundaries are identified by changes in an image's features in a sharp way, such as clipping. Specific objects can be detected, and an improvement in the depth of summarization can also be achieved for videos with a known structure by the analysis of image features. Sports videos are suitable for event detection because of their rigid structure. At the same time, event and object detection can also be achieved in other content areas that present a rigid structure, such as in news-related videos, as the start includes an overview of headlines, then a series of references is displayed, and finally the anchor face is the return [11].
* _Audio features_ are related to the video stream and appear in the audio stream in different ways. Audio features include music, speech, silence, and sounds. Audio features can help identify segments that are candidates for inclusion in a video summary, and improving the depth of the summary can be achieved using domain-specific knowledge [11].
* _Text features_ in the form of text captions or subtitles are displayed in the video. Instead of being a separate stream, text captions are \"burned\" or integrated into the video's image stream. Text may contain detailed information related to the content of the video stream and thus be an important source of information [11]. For example, in a football match broadcast live, captions showing the names of the teams, the score between them, the percentage of possession, the shots on target at that moment, etc., should appear during the match. As with video and audio features, events can also be identified from text. [PERSON] et al. [74] proposed a text-based method. According to the text-based method, video blog posts use supporting texts that are used in the video summary at an earlier time. First, the video is segmented and then, according to the relevance of each segment to the input text, its priority is assigned to the summary video. Then, a subset of segments that have content similar to the content of the input text is selected. Therefore, based on the input text, a video summary is produced.