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Semantic concept detection from news videos with self-organizing maps
- In Proceedings of 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI 2006
, 2006
"... 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 f ..."
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Cited by 4 (4 self)
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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
Focusing Keywords to Automatically Extracted Image Segments Using Self-Organising Maps, volume 210
- of Studies in Fuzziness and Soft Computing
, 2006
"... 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 ..."
Abstract
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Cited by 2 (2 self)
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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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"... ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
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©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
Image Theft Detection with Self-Organising Maps
"... 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 thousan ..."
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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.

