Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier

TL;DR

This paper introduces a unified analysis discriminative dictionary learning framework that efficiently combines dictionary learning, sparse coding, and classification, achieving superior image recognition performance.

Contribution

It proposes a novel ADDL framework that integrates analysis dictionary learning, sparse coding with l2,1-norm, and linear classification into a single efficient model.

Findings

ADDL outperforms existing methods on real image datasets.

The model reduces training time by avoiding iterative sparse reconstruction.

It achieves higher classification accuracy with efficient computation.

Abstract

In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes the training phase time consuming. The codes-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse codes approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state-of-the-arts.