Hey, vitrivr! - A Multimodal UI for Video Retrieval

In this paper, we present a multimodal web-based user interface for the vitrivr system. vitrivr is a modern, open-source video retrieval system for searching in large collections of video using a great variety of query modes, including query-by-sketch, query-by-example and query-by-motion. With the multimodal user interface, prospective users benefit from being able to naturally interact with the vitrivr system by using spoken commands and also by applying multimodal commands which combine spoken instructions with manual pointing. While the main strength of the UI is the seamless combination of speech-based and sketch-based interaction for multimedia similarity search, the speech modality has shown to be very effective for retrieval on its own. In particular, it helps overcoming accessibility boundaries and offering retrieval functionality for users with disabilities. Finally, for a holistic natural experience with the vitrivr system, we have integrated a speech synthesis engine that returns spoken answers to the user

Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

Database support for large-scale multimedia retrieval

With the increasing proliferation of recording devices and the resulting abundance of multimedia data available nowadays, searching and managing these ever-growing collections becomes more and more difficult. In order to support retrieval tasks within large multimedia collections, not only the sheer size, but also the complexity of data and their associated metadata pose great challenges, in particular from a data management perspective. Conventional approaches to address this task have been shown to have only limited success, particularly due to the lack of support for the given data and the required query paradigms. In the area of multimedia research, the missing support for efficiently and effectively managing multimedia data and metadata has recently been recognised as a stumbling block that constraints further developments in the field. In this thesis, we bridge the gap between the database and the multimedia retrieval research areas. We approach the problem of providing a data management system geared towards large collections of multimedia data and the corresponding query paradigms. To this end, we identify the necessary building-blocks for a multimedia data management system which adopts the relational data model and the vector-space model. In essence, we make the following main contributions towards a holistic model of a database system for multimedia data: We introduce an architectural model describing a data management system for multimedia data from a system architecture perspective. We further present a data model which supports the storage of multimedia data and the corresponding metadata, and provides similarity-based search operations. This thesis describes an extensive query model for a very broad range of different query paradigms specifying both logical and executional aspects of a query. Moreover, we consider the efficiency and scalability of the system in a distribution and a storage model, and provide a large and diverse set of index structures for high-dimensional data coming from the vector-space model. Thee developed models crystallise into the scalable multimedia data management system ADAMpro which has been implemented within the iMotion/vitrivr retrieval stack. We quantitatively evaluate our concepts on collections that exceed the current state of the art. The results underline the benefits of our approach and assist in understanding the role of the introduced concepts. Moreover, the findings provide important implications for future research in the field of multimedia data management