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Various query methods for video search exist. Because of the semantic gap each method has its limitations. We argue that for effective retrieval query methods need to be combined at retrieval time. However, switching query methods often involves a change in query and browsing interface, which puts a heavy burden on the user. In this paper, we propose a novel method for fast and effective search trough large video collections by embedding multiple query methods into a single browsing environment. To that end we introduced the notion of query threads, which contain a shot-based ranking of the video collection according to some feature-based similarity measure. On top of these threads we define several thread-based visualizations, ranging from fast targeted search to very broad exploratory search, with the ForkBrowser as the balance between fast search and video space exploration. We compare the effectiveness and efficiency of the ForkBrowser with the CrossBrowser on the TRECVID 2007 interactive search task. Results show that different query methods are needed for different types of search topics, and that the ForkBrowser requires significantly less user interactions to achieve the same result as the CrossBrowser. In addition, both browsers rank among the best interactive retrieval systems currently available.

The growth in available online video material over the Internet is generally combined with user-assigned tags or content description, which is the mechanism by which we then access such video. However, user-assigned tags have limitations for retrieval and often we want access where the content of the video itself is directly matched against a user's query rather than against some manually assigned surrogate tag. Content-based video retrieval techniques are not yet scalable enough to allow interactive searching on Internet-scale, but the techniques are proving robust and effective for smaller collections. In this article, we show three exemplar systems which demonstrate the state of the art in interactive, content-based retrieval of video shots, and these three are just three of the more than 20 systems developed for the 2007 iteration of the annual TRECVid benchmarking activity. The contribution of our article is to show that retrieving from video using content-based methods is now viable, that it works, and that there are many systems which now do this, such as the three outlined herein. These systems, and others can provide effective search on hundreds of hours of video content and are samples of the kind of contentbased search functionality we can expect to see on larger video archives when issues of scale are addressed.

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.

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 [1], our multimedia categorization tool. Me-diaTable 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 Medi-aTable 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.