Abstract. In this paper, we consider the automatic identification of video shots that are relevant to a given semantic concept from large video databases. We apply a method of representing semantic concepts as class models on a set of parallel Self-Organizing Maps trained with multimodal low-level features. The presented experiments were conducted using a set of 170 hours of video containing recorded television news programs. 1

the input data is a collection of images that are annotated with a given keyword, such as “car”. The problem is to attribute the annotation to specific parts of the images. There exists plenty of suitable input data readily

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Abstract. In this paper an application of the TS-SOM variant of the self-organising map algorithm on the problem of copyright theft detection for bitmap images is shown. The algorithm facilitates the location of originals of copied, damaged or modified images within a database of hundreds of thousands of stock images. The method is shown to outperform binary decision tree indexing with invariant frame detection.