Scene classification using bag-of-regions representation
Author
Gökalp, Demir
Advisor
Aksoy, Selim
Date
2007Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Significant growth of multimedia data creates the need for more complicated approaches
in image understanding, classification and retrieval. Semantic scene
classification is a popular research area which categorizes images into semantic
categories for applications like content based image retrieval. In the near future,
content based image retrieval will be much more important especially for the
next generation internet technologies so new approaches are very welcomed in
this subject. Research has showed that classifying images using components like
regions, pixels or objects is a challenging work because of the ambiguity of the
visual data. The main idea about image classification is to find similarities between
these components to get information about the content of the image. This
thesis describes our work on classification of outdoor scenes. As the first step,
regions are extracted using one-class classification and patch-based clustering algorithms.
The components (pixels, regions and objects) in outdoor images have
particular spatial and geometric interactions so dividing images into meaningfully
clustered regions has important benefits for a detailed content analysis. For
region clustering, features from different levels make specific contributions but to
avoid the ambiguity, we need to use low level information and more global information
together for the clustering step. Also, using spatial relationships between
clustered regions, we can make inference about the detailed content of outdoor
images from specific to general. Therefore, after rough segmentation, scene representations
are constructed with and without spatial information. At the final
step Bayesian classification approach is used with the two different scene representations.
The developed methods were tested on the MIT LabelMe dataset,
and the results showed that using regions and their spatial relationships improved
the classification accuracy.