Automatic Image Annotation using a Visual Dictionary based on reliable Image Segmentation

Abstract : Recent approaches in Automatic Image Annotation (AIA) try to combine the expressiveness of natural language queries with approaches to minimize the manual effort for image annotation. The main idea is to infer the annotations of unseen images using a small set of manually annotated training examples. However, typically these approaches suffer from low correlation between the globally assigned annotations and the local features used to obtain annotations automatically. In this paper we propose a framework to support image annotations based on a visual dictionary that is created automatically using a set of locally annotated training images. We designed a segmentation and annotation interface to allow for easy annotation of the traing data. In order to provide a framework that is easily extendable and reusable we make broad use of the MPEG-7 standard.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01335955
Contributor : Lip6 Publications <>
Submitted on : Wednesday, June 22, 2016 - 3:05:11 PM
Last modification on : Thursday, March 21, 2019 - 2:43:03 PM

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Christian Hentschel, Sebastian Stober, Andreas Nürnberger, Marcin Detyniecki. Automatic Image Annotation using a Visual Dictionary based on reliable Image Segmentation. The International Workshop on Adaptive Multimedia Retrieval - AMR'2007, 2007, Paris, France. pp.45-56, ⟨10.1007/978-3-540-79860-6_4⟩. ⟨hal-01335955⟩

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