Data-driven dictionaries have produced state-of-the-art results in various classification tasks.
However, when the target data has a different distribution than the source data, the learned …

Data-driven dictionaries have produced the state-of-the-art results in various classification
tasks. However, when the target data has a different distribution than the source data, the …

Dictionary learning has produced state-of-the-art results in various classification tasks.
However, if the training data have a different distribution than the testing data, the learned …

Many recent efforts have shown the effectiveness of dictionary learning methods in solving
several computer vision problems. However, when designing dictionaries, training and …

Object recognition is challenging especially when the objects from different categories are
visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) …

Empirically, we find that despite the most exclusively discriminative features owned by one
specific object category, the various classes of objects usually share some common …

The sparse coding technique has shown flexibility and capability in image representation
and analysis. It is a powerful tool in many visual applications. Some recent work has shown …

In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe
different aspects of the images, for obtaining an improved recognition performance …

Empirically, we find that, despite the class-specific features owned by the objects appearing
in the images, the objects from different categories usually share some common patterns …

Many techniques in computer vision, machine learning, and statistics rely on the fact that a
signal of interest admits a sparse representation over some dictionary. Dictionaries are …