7,070 research outputs found
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated
for image classification problems. To further sharpen their discriminative
capabilities, most state-of-the-art DL methods have additional constraints
included in the learning stages. These various constraints, however, lead to
additional computational complexity. We hence propose an efficient
Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a
lower cost Discriminative DL framework, to both characterize the image
structures and refine the interclass structure representations. The proposed
DCADL jointly learns a convolutional analysis dictionary and a universal
classifier, while greatly reducing the time complexity in both training and
testing phases, and achieving a competitive accuracy, thus demonstrating great
performance in many experiments with standard databases.Comment: ICASSP 201
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to
restoration tasks and can effectively be learned from audio, image, and video
data. Recent research has been aimed at learning discriminative sparse models
instead of purely reconstructive ones. This paper proposes a new step in that
direction, with a novel sparse representation for signals belonging to
different classes in terms of a shared dictionary and multiple class-decision
functions. The linear variant of the proposed model admits a simple
probabilistic interpretation, while its most general variant admits an
interpretation in terms of kernels. An optimization framework for learning all
the components of the proposed model is presented, along with experimental
results on standard handwritten digit and texture classification tasks
- …