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Low-Level Image Segmentation Based Scene Classification

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Low-Level Image Segmentation Based Scene Classification
This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model the image in terms of a probability density function, a Gaussian mixture model (GMM) to be specific, of its region features. This GMM is fit to the image by adapting a universal GMM which is estimated so it fits all images. Adaptation is done using a maximum-aposteriori criterion. We use kernelized versions of Bhattacharyya distance to measure the similarity between two GMMs and support vector machines to perform classification. We outperform previously reported results on a publicly ...
Emre Akbas, Narendra Ahuja
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICPR
Authors Emre Akbas, Narendra Ahuja
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