Multiple-Instance Learning for Natural Scene Classification
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Summary
It is shown that very simple templates are suu-cient, and that performance improves with more user interaction, and the Diverse Density algorithm which is a method of learning from ambiguous examples is discussed.
- Type
- article
- Published
- 1998-07-24
- Cited by
- 667
- References
- 26
- OpenAlex
- https://openalex.org/W1540386283
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:39240439
Keywords
Computer science, Artificial intelligence, Natural (archaeology), Machine learning, Pattern recognition (psychology)
References
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- PicHunter: Bayesian relevance feedback for image retrieval
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- Query by Image and Video Content: The QBIC System
- A Framework for Multiple-Instance Learning
- Color- and texture-based image segmentation using EM and its application to content-based image retrieval
- Interactive Learning Using a "Society of Models"
- AND T
- A Note on the
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- Vers une description efficace du contenu visuel pour l'annotation automatique d'images. (Towards an efficient visual content description for images automatic annotation)
- Annotation semi-automatique de grandes BD images : Approche par graphes de voisinage
- Semi-Supervised Learning with Partially Labeled Examples
- Towards an Effective Semi-Automatic Technique for Image Annotation
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- Data as Ensembles of Records: Representation and Comparison
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- Préparation non paramétrique des données pour la fouille de données multi-tables
- Experiments with multi-view multi-instance learning for supervised image classification
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