# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 5 Task 3: Intelligent Narrative Discovery
### Image Collection Summarization  
Intent discovery, mentioned in the previous section, facilitates the image collection summarization process and determines what or how the data are summarized. Existing techniques for storytelling with image data may be split into two categories: vision-based techniques and text-based techniques [101]. Vision-based methods rebuild successive pictures or frames based on a narrative or plot. Visual summarization chiefly consists of selecting important frames or pictures from a series to create a plot. The vision and text techniques primarily focus on summarizing or sorting consecutive pictures or frames and are a shallower task than what is often referred to as \"storytelling\" [101]. Following [PERSON] et al. [102], the number of online picture collections is rapidly growing, and it has resulted in an explosion of photos and an accumulation of multimedia data. They also discussed that managing this massive quantity of data is a significant challenge, and novel strategies are necessary to aid users in viewing, browsing, and summarizing these massive collections. Techniques for image summarizing might be classified into two broad categories: visual summarization and multimodal summarization. The term \"visual summarizing approaches\" refers to a type of work that uses exclusively visual aspects as its summary features. Multimodal image collection summarizing approaches often incorporate additional modalities such as textual, geospatial, or other data types. **Visual Collection Summarization. ** [PERSON] et al. [37] discussed summarizing an image collection as a critical undertaking that continues to escape practitioners due to the inherent difficulties and distinctions between image collection summarizing and video collection summarizing. Due to the fact that the video contains numerous temporal links that can be utilized using temporal neural networks such as Long Short-Term Memory (LSTM) or Recurrent Neural Networks (RNNs), they confirm to be advantageous when constructing DL-based architectures for the incident and video summarization. As [PERSON] et al. [103] mentioned, when we use digital cameras, we usually end up with repeating and similar photographs that people wish to delete. Manually sorting through such a vast collection and selecting the finest photographs is time-consuming. [PERSON] et al. [103] suggested a method for summarizing a collection of pictures into a unique set of representative images. They partitioned the photos into a \"Bag of words\" representation constructed using Scale-Invariant Feature Transform (SIFT) vectors and then clustered these vectors into categories using a simplified Latent Dirichlet Allocation (LDA) approach. They portrayed each photo as a \"bag of words\" using SIFT vectors by clustering these vectors into multiple bins and displaying each image as a histogram of the probability density of these vectors over such bins. Finally, they classified the picture collection into clusters using the topic modeling LDA approach. The image vector determines the best image with the smallest sum of square distances inside the cluster from the other image vectors [103]. [PERSON] et al. [104] presented a technique for incorporating domain knowledge into summarizing large image sets. They employed a multiclass kernel alignment technique to train a kernel function that takes domain knowledge into account. The kernel function serves as the foundation for a clustering technique that creates a subset of the visual collection, termed the summary. Features are retrieved from the remaining pictures, and the kernel function is computed using the previously acquired combination function. Following them, when a new image is added to the collections, it may be categorized into one of the clusters by calculating its resemblance to the medoids utilizing the combination function. [PERSON] et al. [105] developed two unsupervised approaches for extractive image summarizing.