Geo-based automatic image annotation
| Autor: | Jean-Marie Pinon, Harald Kosch, Gabriele Gianini, Mario Döller, Elöd Egyed-Zsigmond, Hatem Mousselly Sergieh |
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| Přispěvatelé: | Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), University of Passau |
| Rok vydání: | 2012 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology Automatic image annotation Image texture 020204 information systems Digital image processing 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Computer vision Visual Word Artificial intelligence Image analysis business Image retrieval Feature detection (computer vision) |
| Zdroj: | ICMR 2nd ACM International Conference on Multimedia Retrieval (ICMR '12) 2nd ACM International Conference on Multimedia Retrieval (ICMR '12), Jun 2012, Hong Kong, China. pp.1-8, ⟨10.1145/2324796.2324850⟩ |
| DOI: | 10.1145/2324796.2324850 |
| Popis: | International audience; A huge number of user-tagged images are daily uploaded to the web. Recently, a growing number of those images are also geotagged. These provide new opportunities for solutions to automatically tag images so that efficient image management and retrieval can be achieved. In this paper an automatic image annotation approach is proposed. It is based on a statistical model that combines two different kinds of information: high level information represented by user tags of images captured in the same location as a new unlabeled image (input image); and low level information represented by the visual similarity between the input image and the collection of geographically similar images. To maximize the number of images that are visually similar to the input image, an iterative visual matching approach is proposed and evaluated. The results show that a significant recall improvement can be achieved with an increasing number of iterations. The quality of the recommended tags has also been evaluated and an overall good performance has been observed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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