The political landscape is fluid. Discussions are always ongoing and new "hot topics" continue to appear in the headlines. But what made people start talking about that topic? And who started it? Because of the speed at which discussions sometimes take place this can be difficult to track down. We describe ThemeStreams: a demonstrator that maps political discussions to themes and influencers and illustrate how this mapping is used in an interactive visualization that shows us which themes are being discussed, and that helps us answer the question "Who put this issue on the map?" in streams of political data.

There are large amounts of digital video available. High recall retrieval of these requires going beyond the ranked results, which is the common target in high precision retrieval. To aid high recall retrieval, we propose Active Bucket Categorization, which is a multicategory interactive learning strategy which extends MediaTable, our multimedia categorization tool. MediaTable allows users to place video shots into buckets: user-assigned subsets of the collection. Our Active Bucket Categorization approach augments this by unobtrusively expanding these buckets with related footage from the whole collection. In this paper, we propose an architecture for active bucket-based video retrieval, evaluate two different learning strategies, and show its use in video retrieval with an evaluation using three groups of nonexpert users. One baseline group uses only the categorization features of MediaTable such as sorting and filtering on concepts and fast grid preview, but no online learning mechanisms. One group uses on-demand passive buckets. The last group uses fully automatic active buckets which autonomously add content to buckets. Results indicate a significant increase in the number of relevant items found for the two groups of users using bucket expansions, yielding the best results with fully automatic bucket expansions, thereby aiding high recall video retrieval significantly.

Currently there are several interactive content-based video retrieval techniques and systems available. However, retrieval performance depends heavily on the means of interaction. We argue that effective CBVR requires efficient, specialized user interfaces. In this article we propose guidelines for such an interface, and we propose an effective CBVR engine: the ForkBrowser, which builds upon the principle of focus and context. This browser is evaluated using a combination of user simulation and real user evaluation. Results indicate that the ideas have merit, and that the browser performs very well when compared to the state-of-the-art in video retrieval.

This demo showcases our realtime implementation of concept classification using the Bag-of-Words method embedded within MediaTable, our interactive categorization tool for large multimedia collections. MediaTable allows the users to open images from disk or download these directly from the internet. Each image is then processed using the Bag-of-Words method, which computes classification scores for 20 distinct concepts classes on the fly. These are then seamlessly displayed in the interface.

In this technical demonstration, we present MediaTable, our interactive multimedia collection search and categorization tool. MediaTable allows users to search through, and categorize a multimedia collection with ease by employing several familiar interface components specifically adapted for multimedia collections. In our demonstration we expand on the visual interface, on how several types of search tasks can be completed with MediaTable.

This paper describes a novel method for browsing a large video collection. It links various forms of related video fragments together as threads. These threads are based on query results, the timeline as well as visual and semantic similarity. We design two interfaces which use threads as the basis for browsing. One interface shows a minimal set of threads, and the other as many as fit on the screen. To evaluate both interfaces we perform a regular user study, a study based on user simulation, and we participated in the interactive video retrieval task of the TRECVID benchmark. The results indicate that the use of threads in interactive video retrieval is beneficial. Furthermore, we found that in general the query result and the timeline are the most important threads, but having several additional threads improves the performance as it encourages people to explore new dimensions.

In this paper we present the methods underlying the MediaMill semantic video search engine. The basis for the engine is a semantic indexing process which is currently based on a lexicon of 491 concept detectors. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. We compare the different browsers and their utility within the TRECVID benchmark. In 2005, we obtained a top-3 result for 19 out of 24 search topics. In 2006 for 14 out of 24.

In this technical demonstration we showcase the RotorBrowser. A visualization within MediaMill system which uses query exploration as the basis for search in video archives.

This paper describes a novel method for browsing a large collection of news video by linking various forms of related video fragments together as threads. Each thread contains a sequence of shots with high feature-based similarity. Two interfaces are designed which use threads as the basis for browsing. One interface shows a minimal set of threads, and the other as many as possible. Both interfaces are evaluated in the TRECVID interactive retrieval task, where they ranked among the best interactive retrieval systems currently available. The results indicate that the use of threads in interactive video search is very beneficial. We have found that in general the query result and the timeline are the most important threads. However, having several additional threads allow a user to find unique results which cannot easily be found by using query results and time alone.

In this technical demonstration we show the current version of the MediaMill system, a search engine that facilitates access to news video archives at a semantic level. The core of the system is a lexicon of 436 automatically detected semantic concepts. To handle such a large lexicon in retrieval, an engine is developed which automatically selects a set of relevant concepts based on the textual query and example images. The result set can be browsed easily to obtain the final result for the query.

In this technical demonstration we showcase the MediaMill system. A search engine that facilitates access to news video archives at a semantic level. The core of the system is an unprecedented lexicon of 100 automatically detected semantic concepts. Based on this lexicon we demonstrate how users can obtain highly relevant retrieval results using query-by-concept. In addition, we show how the lexicon of concepts can be exploited for novel applications using advanced semantic visualizations. Several aspects of the MediaMill system are evaluated as part of our TRECVID 2005 efforts.