Information-theoretic Dictionary Learning for Image Classification
This work addresses image classification tasks, but it appears incremental as it builds on existing dictionary learning methods with a focus on information-theoretic principles.
The authors tackled the problem of learning dictionaries for image classification by proposing a two-stage method based on information maximization to create compact, discriminative, and generative dictionaries, and demonstrated its effectiveness on real datasets.
We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real datasets demonstrate the effectiveness of our approach for image classification tasks.