We use features of segmentation for semantic classification of real images. 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 classifcation.
- E. Akbas and N. Ahuja, Low-level Image Segmentation Based Scene Classification, International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010.