# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 1 Introduction  
Due to the advancement of technology, a massive amount of data is generated on various offline/online platforms. Data analysis and Big Data are at the forefront of business and science today. These data are collected from online transactions, emails, images, videos, audio files, log files, posts, health records, social networking interactions, scientific data, sensors, and mobile phones. However, this Big Data is stored in different data islands that grow immensely and are challenging to capture, store, organize, analyze, and visualize. The challenges that big data carry include understanding the data, poor data quality, data scaling, incorrect integration, considerable costs, real-time data issues, and data verification. A substantial amount of this unstructured generated data consists of images and videos, e.g., on Google Images and surveillance cameras. Moreover, the emergence of social media has resulted in countless daily images uploaded by users on numerous online platforms, such as Instagram and Facebook [1]. Therefore, managing, curating, processing, and analyzing this vast amount of data is of significant importance. To be more specific, data analysis can help businesses better understand their customers, increase sales, improve customer targeting, diminish costs, and solve problems more effectively. It is usedto evaluate data with statistical tools to discover useful information. Therefore, analyzing data is crucial in many organizations, businesses, and research studies, as well as academia. Images are one of the essential categories among the vast available data. There are various means of processing and analyzing image data by assigning keywords, detecting objects, annotating, captioning, or creating a textual description. Thus, there has been an explosion of interest in this area, which is used in various applications such as image annotation, object detection, and image captioning. Various traditional computer vision approaches have been proposed for solving these tasks; however, they have limited capabilities. It is necessary to choose which features are important in each given image in traditional methods (feature selection step). The traditional approaches use well-established computer vision (CV) techniques such as feature descriptors (e.g., SIFT [2], SURF [3], BRIEF [4]), and after that, a feature extraction step is carried out. The features in an image are small areas of interest that are descriptive or informative. Several computer vision algorithms may be involved in this step, such as edge detection, corner detection, or threshold segmentation. In recent years, machine learning (ML) and deep learning (DL) have been recognized as highly effective means for advancing technology and have pushed the limits of what is possible in the domain of image processing and artificial intelligence (AI) in general. As a result, ML and DL are increasingly applied to machine vision applications. Furthermore, multiple problems in vision tasks (e.g., object detection, image annotation, and multilabel image classification) have leveraged these learning techniques to interpret an image to figure out what is occurring. Recent DL-based approaches have demonstrated promising results in vision tasks, and the introduction of convolutional networks has set off a technological revolution in \"image understanding\". In the last decade, we have witnessed a significant advancement in the field of image processing, analytics, and understanding. We can discover patterns in raw image data using data analytics techniques and gain valuable insights. However, these techniques have limitations, such as hidden bias and complexity, which affects the decision process. On the other hand, they do not provide sufficient techniques for discovering, linking, and understanding image data. Transforming the data extracted from images into commonly understood and subjective descriptions to make subsequent decisions according to the various interpretations of the image data is challenging.