Image Segmentation Using Dictionary Learning and Compressed Random Features

Image segmentation seeks to partition the pixels in images into distinct regions to assist other image processing functions such as object recognition. Over the last few years dictionary learning methods have become very popular for image processing tasks such as denoising, and recently structured low rank dictionary learning has been shown to be capable of promising results for recognition tasks. This paper investigates the suitability of dictionary learning for image segmentation. A structured low rank dictionary learning algorithm is developed to segment images using compressed sensed features from image patches. To enable a supervised learning approach, classes of pixels in images are designated using training scribbles. A classifier is then learned from these training pixels and subsequently used to classify all other pixels in the images to form the segmentations. A number of dictionary learning models are compared together with K-means/nearest neighbour and support vector machine classifiers.