# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 1 Introduction
Despite the early adoption, storytelling systems are still in the early stages of development, with many unsolved theoretical and technical challenges stemming from the lack of a formal framework for story modeling and creation from data identifying the notion of story schema, story instance, story element, and narrative. Most related works [6; 7] in storytelling presented interactive visualizations to convey data-driven discoveries in a captivating and intuitive way. However, storytelling with image data is  
Figure 1: An end-to-end storytelling example. Storytelling with image data could be a critical tool in preventing events like the 2016 Brussels bombings. By analyzing and connecting images from various sources, including security cameras and social media, authorities can identify potential threats and act preemptively to ensure public safety. much more than sophisticated ways of presenting data visually. Organizing image data, contextualizing it, enhancing the discovery of related events and entities in the image, and presenting it interactively to end-users are the main challenges in storytelling with image data. However, more importantly, there is a need to evaluate the quality of the constructed stories and narratives, which is still a big challenge. We further discuss the current methods for assessing the quality of generated stories in Section 8; however, we leave it as future work since there is a huge knowledge gap in this part and it is still an active research field. In this survey, we focus on the research problem of \"understanding the image data\" in general and, more particularly, analyzing the state of the art in the curation, summarization, and presentation of large amounts of image data in a succinct and consumable manner to end-users. We argue that stories are able to combine data with narratives to reduce the ambiguity of data, connect data with the context, and describe a specific interpretation. We aim to advance the scientific understanding of storytelling with image data in general, and image data curation, event/entity extraction and discovery, narrative formation (e.g., event stream processing), Knowledge Bases [8], Knowledge Lakes [9], and Knowledge Graphs [10; 11] in particular. We provide a systematic review and comparative analysis of methods and tools to identify, evaluate, and interpret relevant research in the field of storytelling with image data. This paper introduces storytelling as a process that facilitates understanding image data. This process uses several phases, from cleaning and cleansing, followed by curation and adding value. Several approaches in this paper focus on preparing an image for data analytics and turning a raw image into contextualized knowledge, which is very critical for the process of storytelling with image data. Therefore, we decided to conduct a comprehensive review of the existing work in image processing, cleaning, and curation, preparing the understanding of the importance of these phases in the storytelling process. These phases and steps of storytelling with image data are highlighted in Figure 2.  
Figure 2: The proposed taxonomy for storytelling with image data. The method used to select the literature for this survey is described in Section 2. Storytelling with image data is decomposed into five tasks. Data curation is the first task toward story generation with image data, which is discussed in Section 3.1. Then, in Section 4, we investigate the story modeling process. Intelligent narrative discovery is further studied in Section 5. Story creation and intelligent widgets for narrative discovery are discussed in Sections 6 and 7. Section 8 explains the quality assessment step. Finally, in Section 9, we conclude our study and provide the future works that we have in mind. The taxonomy of storytelling with image data is demonstrated in Figure 2.