# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Features
#### 4.2.1 Object Based  
Object-based techniques focus on objects that are specific and present in the video. Techniques are useful in detecting one or more objects, such as a table, a dog, a person, etc. Some object-based summaries may include graphics or text to simplify the process of selecting video segments or keys easier, or to represent the objects contained in the video summary based on the detection of those objects [11]. Video summarization can be performed by collecting all frames with the desired object from the video. If the video does not include some type of desired object, then this method, although executed, will not be effective [37].  
In the literature, there are several studies in which the creation of a personalized video summary is based on the user's query, which is taken into consideration [12; 13; 19; 30; 48; 52; 55; 64; 65; 66; 68; 69; 70; 73]. [PERSON] and [PERSON] [8] proposed a personalized video summarization approach using classification-based entity identification. They succeeded in creating personalized summaries of desired lengths, thanks to the compression, recognition, and special classification of the macros present in the video. After the completion of video segmentation, the method of [PERSON] et al. [74] selects video segments based on their objects. Each video segment is represented by object tags and their meaning is included. The Object of Interest (OoI), defined by [PERSON] et al. [59], is the user's interest features. They suggested an algorithm that summarizes a video that focuses on a given OoI with the key option oriented to the user's interest. [PERSON] et al. [33] proposed a framework in which, using  
Figure 5: Distribution of the papers reported in Table 1 according to the type of personalized summary technique.  
deep learning technology, frames with Ools are detected, which are combined to produce a video digest as output. This framework can detect one or more objects presented in the video. [PERSON] et al. [25] proposed a framework for egocentric video summarization that uses unsupervised reinforcement learning and 3D convolutional neural networks (CNNs) that incorporate user preferences such as places, faces, and user choice in an interactive way to exclude or include that type of content. [PERSON] et al. [16] proposed a method to generate personalized video summaries based on sketch selection that relies on the generation of a graph from keyframes. The features of the sketches are represented by the generated graph. The proposed algorithm interactively selects those frames related to the same person so that there is direct user interaction with the video content. Depending on the different requirements of each user, the interaction and design are carried out with gestures.  
The body-based method presented by [PERSON] et al. [45], which focuses on the characteristics of the body joint based on these characteristics of human appearance, has little variability, and human movement is well represented. The method based on a neural network uses the actions of the players by combining holistic features and body joints so that the most important points can be extracted from the original video. Two types of action-related features are extracted and then classified as uninteresting or interesting. The personalized video summaries presented in [14; 17; 26; 44; 46; 53; 54; 58; 60; 62; 66; 67] are also object-based.