Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
This work addresses image classification for computer vision applications, but it is incremental as it builds on existing sparse representation and dictionary learning techniques.
The authors tackled supervised pixelwise image classification by learning multilevel tree-structured dictionaries with discriminative constraints, achieving competitive results with state-of-the-art methods on a common set of texture images.
Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, compression or classification. The sparsity of the representation very much depends on how well the dictionary is adapted to the data at hand. In this paper, we propose a method for learning structured multilevel dictionaries with discriminative constraints to make them well suited for the supervised pixelwise classification of images. A multilevel tree-structured discriminative dictionary is learnt for each class, with a learning objective concerning the reconstruction errors of the image patches around the pixels over each class-representative dictionary. After the initial assignment of the class labels to image pixels based on their sparse representations over the learnt dictionaries, the final classification is achieved by smoothing the label image with a graph cut method and an erosion method. Applied to a common set of texture images, our supervised classification method shows competitive results with the state of the art.