Automatic image annotation by ensemble of visual descriptors

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2006

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Akbaş, Emre

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Automatic image annotation is the process of automatically producing words to de- scribe the content for a given image. It provides us with a natural means of semantic indexing for content based image retrieval. In this thesis, two novel automatic image annotation systems targeting dierent types of annotated data are proposed. The rst system, called Supervised Ensemble of Visual Descriptors (SEVD), is trained on a set of annotated images with predened class labels. Then, the system auto- matically annotates an unknown sample depending on the classication results. The second system, called Unsupervised Ensemble of Visual Descriptors (UEVD), assumes no class labels. Therefore, the annotation of an unknown sample is accomplished by unsupervised learning based on the visual similarity of images. The available auto- matic annotation systems in the literature mostly use a single set of features to train a single learning architecture. On the other hand, the proposed annotation systems utilize a novel model of image representation in which an image is represented with a variety of feature sets, spanning an almost complete visual information comprising color, shape, and texture characteristics. In both systems, a separate learning entity is trained for each feature set and these entities are gathered under an ensemble learning approach. Empirical results show that both SEVD and UEVD outperform some of the state-of-the-art automatic image annotation systems in equivalent experimental setups.

Subject Keywords

Computer Science.

URI

http://etd.lib.metu.edu.tr/upload/3/12607443/index.pdf
https://hdl.handle.net/11511/16027

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Akbaş, “Automatic image annotation by ensemble of visual descriptors,” M.S. - Master of Science, Middle East Technical University, 2006.