Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
The traditional SPM approach based on bag-of-features (BoF) must use nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effec...
In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block...
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
—We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and thei...