Beyond Relevance Feedback for Searching and Exploring large Multimedia Collections

Relevance feedback was introduced over twenty years ago as a powerful tool for interactive retrieval and still is the dominant mode of interaction in multimedia retrieval systems. Over the years methods have improved and recently relevance feedback has become feasible on even the largest collections available in the multimedia community. Yet, relevance feedback typically targets the optimization of linear lists of search results and thus focuses on only one of the many tasks on the search - explore axis. Truly interactive retrieval systems have to consider the whole axis and interactive categorization is an overarching framework for many of those tasks. The multimedia analytics system MediaTable exploits this to support users in getting insight in large image collections. Categorization as a representation of the collection and user tasks does not capture the relations between items in the collection like graphs do. Hypergraphs are combining categories and relations in one model and as they are founded in set theory in fact are closely related to categorization. They, therefore, provide an elegant framework to move forward. In this talk we highlight the progress that has been made in the field of interactive retrieval and in the direction of multimedia analytics. We will further consider the promises that new results in deep learning, especially in the context of graph convolutional networks, and hypergraphs might bring to go beyond relevance feedback.