The vitrivr multimedia retrieval stack has been shown to be versatile and effective in many areas and application domains. In this paper, we present vitrivr's new retrieval engine, the vitrivr-engine, a flexible and highly configurable information extraction and query processing solution with support for a broad range of media types.

The field of information retrieval, especially when targeting multimodal content, has found ways of satisfying a broad range of information needs, which can be expressed in a multitude of ways. In contrast to related fields, such as relational databases, no universal way of representing the queries to be answered by a retrieval system has emerged. In this paper, we present an initial proposal for a universal query representation mechanism for multimodal information retrieval. The proposed approach imperatively expresses arbitrary information needs, using a DAG of query primitives. We show how such a representation can be used for both feature extraction and query processing pipelines and how it can serve as a foundation towards a query language for information retrieval.

Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for the retrieval process itself and the subsequent verification of results. Such experiments can be elaborate and use-case-specific, which can make them difficult to compare or replicate. In this article, we present a formal model to express all relevant aspects of such retrieval experiments, as well as a flexible open-source evaluation infrastructure that implements the model. These contributions intend to make a step towards lowering the hurdles for conducting retrieval experiments and improving their reproducibility.

The way we create, consume and interact with multimedia content has changed significantly in recent years with the advent of affordable recording devices and easy sharing and access in the form of mobile phones. With the imminent wave of affordable devices that enable mixed reality experiences and the large variety of devices on the market, interaction with multimedia content is expected to continue to evolve rapidly. This will also drastically affect the entire area of multimedia information retrieval in eXtended Reality (XR), for instance by novel ways to express user needs in VR, result presentation that takes the specific capabilities of XR devices into account, and/or result feedback. This tutorial on Multimedia Retrieval in XR discusses and demonstrates existing solutions and highlights key challenges in this evolving field.

Interactive retrieval with user-friendly and performant interfaces remains a necessity for video retrieval, even in light of significant gains in retrieval performance through multi-modal encoders. In recent years, novel interaction modalities such as virtual reality (VR) and augmented reality (AR) have gained popularity, but the best way to adapt paradigms from traditional retrieval interfaces, especially for result browsing and interaction, remains an open research question. In this paper, we compare two video retrieval interfaces in a controlled setting to gain insight into the differences in video browsing between VR and desktop interfaces. We formulate hypotheses explaining why there might be performance differences between the two interfaces, define metrics to test the hypotheses, and show results based on data gathered at an evaluation campaign. Our results show that VR interfaces can be competitive in browsing performance and indicate that there can even be an advantage when browsing larger result sets in VR.

Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.

Multimedia retrieval and analysis are two important areas in "Big data" research. They have in common that they work with feature vectors as proxies for the media objects themselves. Together with metadata such as textual descriptions or numbers, these vectors describe a media object in its entirety, and must therefore be considered jointly for both storage and retrieval.

The steady growth of multimedia collections - both in terms of size and heterogeneity - necessitates systems that are able to conjointly deal with several types of media as well as large volumes of data. This is especially true when it comes to satisfying a particular information need, i.e., retrieving a particular object of interest from a large collection. Nevertheless, existing multimedia management and retrieval systems are mostly organized in silos and treat different media types separately. Hence, they are limited when it comes to crossing these silos for accessing objects. In this paper, we present vitrivr, a general-purpose content-based multimedia retrieval stack. In addition to the keyword search provided by most media management systems, vitrivr also exploits the object's content in order to facilitate different types of similarity search. This can be done within and, most importantly, across different media types giving rise to new, interesting use cases. To the best of our knowledge, the full vitrivr stack is unique in that it seamlessly integrates support for four different types of media, namely images, audio, videos, and 3D models.

This work summarizes the findings of the 7th iteration of the Video Browser Showdown (VBS) competition organized as a workshop at the 24th International Conference on Multimedia Modeling in Bangkok. The competition focuses on video retrieval scenarios in which the searched scenes were either previously observed or described by another person (i.e., an example shot is not available). During the event, nine teams competed with their video retrieval tools in providing access to a shared video collection with 600 hours of video content. Evaluation objectives, rules, scoring, tasks, and all participating tools are described in the article. In addition, we provide some insights into how the different teams interacted with their video browsers, which was made possible by a novel interaction logging mechanism introduced for this iteration of the VBS. The results collected at the VBS evaluation server confirm that searching for one particular scene in the collection when given a limited time is still a challenging task for many of the approaches that were showcased during the event. Given only a short textual description, finding the correct scene is even harder. In ad hoc search with multiple relevant scenes, the tools were mostly able to find at least one scene, whereas recall was the issue for many teams. The logs also reveal that even though recent exciting advances in machine learning narrow the classical semantic gap problem, user-centric interfaces are still required to mediate access to specific content. Finally, open challenges and lessons learned are presented for future VBS events.

The game of Nine Men's Morris is a draw. We obtained this result using a combination of endgame databases (1010 states) and search. Our improved algorithm for computing endgame databases allowed the game to be solved on a personal computer. Other games have been solved using knowledge‐based methods to dramatically prune the search tree. Nine Men's Morris does not seem to profit from such methods, making it the first nontrivial game solved in which almost the entire state space has to be considered.