The past decade has seen the rapid proliferation of low-priced devices for recording image, audio and video data in nearly unlimited quantity. Multimedia is Big Data, not only in terms of their volume, but also with respect to their heterogeneous nature. This also includes the variety of the queries to be executed. Current approaches for searching in big multimedia collections mainly rely on keywords. However, manually annotating every single object in a large collection is not feasible. Therefore, content-based multimedia retrieval -using sample objects as query input-is increasingly becoming an important requirement for dealing with the data deluge. In image databases, for instance, effective methods exploit the use of exemplary images or hand-drawn sketches as query input. In this paper, we introduce ADAM, a novel multimedia retrieval system that is tailored to large collections and that is able to support both Boolean retrieval for structured data and similarity-based retrieval for feature vectors extracted from the multimedia objects. For efficient query processing in such big multimedia data, ADAM allows the distribution of the indexed collection to multiple shards and performs queries in a MapReduce style. Furthermore, it supports a signature-based indexing strategy for similarity search that heavily reduces the query time. The efficiency of ADAM has been successfully evaluated in a content-based image retrieval application on the basis of 14 million images from the ImageNet collection.

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Searching for scenes in team sport videos is a task that recurs very often in game analysis and other related activities performed by coaches. In most cases, queries are formulated on the basis of specific motion characteristics the user remembers from the video. Providing sketching interfaces for graphically specifying query input is thus a very natural user interaction for a retrieval application. However, the quality of the query (the sketch) heavily depends on the memory of the user and her ability to accurately formulate the intended search query by transforming this 3D memory of the known item(s) into a 2D sketch query. In this paper, we present an auto-suggest search feature that harnesses spatiotemporal data of team sport videos to suggest potential directions containing relevant data during the formulation of a sketch-based motion query. Users can intuitively select the direction of the desired motion query on-the-fly using the displayed visual clues, thus relaxing the need for relying heavily on memory to formulate the query. At the same time, this significantly enhances the accuracy of the results and the speed at which they appear. A first evaluation has shown the effectiveness and efficiency of our approach.

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The tremendous increase of multimedia data in recent years has heightened the need for systems that not only allow to search with keywords, but that also support content-based retrieval in order to effectively and efficiently query large collections. In this paper, we introduce ADAM, a system that is able to store and retrieve multimedia objects by seamlessly combining aspects from databases and information retrieval. ADAM is able to work with both structured and unstructured data and to jointly provide Boolean retrieval and similarity search. To efficiently handle large volumes of data it makes use of a signature-based indexing and the distribution of the collection to multiple shards that are queried in a MapReduce style. We present ADAM in the setting of a sketch-based image retrieval application using the ImageNet collection containing 14 million images.