A regularization-based approach for unsupervised image segmentation
This addresses the problem of unsupervised image segmentation for computer vision applications, offering an incremental improvement by reducing oversegmentation while maintaining precision.
The paper tackles unsupervised image segmentation by proposing an algorithm that iteratively merges superpixels using a discriminative classifier regularized by a Markov random field, achieving performance on par with state-of-the-art in precision and greatly reducing oversegmentation.
We propose a novel unsupervised image segmentation algorithm, which aims to segment an image into several coherent parts. It requires no user input, no supervised learning phase and assumes an unknown number of segments. It achieves this by first over-segmenting the image into several hundred superpixels. These are iteratively joined on the basis of a discriminative classifier trained on color and texture information obtained from each superpixel. The output of the classifier is regularized by a Markov random field that lends more influence to neighbouring superpixels that are more similar. In each iteration, similar superpixels fall under the same label, until only a few coherent regions remain in the image. The algorithm was tested on a standard evaluation data set, where it performs on par with state-of-the-art algorithms in term of precision and greatly outperforms the state of the art by reducing the oversegmentation of the object of interest.