Tomas Homola

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The complexity of search in current business intelligence systems, academic research, or even the home audiovisual databases grows up rapidly. Users require searching by the content of their data. For example, the user sees a cathedral while watching a movie and by taking a snapshot, his or her private collection of holiday photos can be searched for images(More)
As the volume of non-textual data, such images and other multimedia data, available on Internet is increasing. The issue of identifying data items based on query containment rather than query equality is becoming more and more important. In this paper, we propose a solution to this problem. We assume local descriptors are extracted from data items, so the(More)
Sub-image content-based similarity search forms an important operation in current image archives since it provides users with images that contain a query image as their part. Such a search can conveniently be implemented using the bag-of-features model. Its integral part is a construction of visual vocabulary. Most existing algorithms to create a visual(More)
—The availability of various photo archives and photo sharing systems made similarity searching much more important because the photos are not usually conveniently tagged. So the photos (images) need to be searched by their content. Moreover, it is important not only to compare images with a query holistically but also to locate images that contain the(More)
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