# Class specific or shared? A cascaded dictionary learning framework for image classification

## TL;DR

This paper introduces a cascaded dictionary learning framework that combines class-specific and shared dictionaries to improve image classification accuracy, demonstrating superior results on multiple benchmark datasets.

## Contribution

The paper proposes a novel label embedded dictionary learning method and a cascaded framework that effectively integrates class shared and specific dictionaries for enhanced classification.

## Key findings

- Achieved superior classification performance on six benchmark datasets.
- Proposed LEDL improves upon LCKSVD for dictionary learning.
- Cascaded framework effectively boosts classification accuracy.

## Abstract

Dictionary learning methods can be split into: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use discriminative information. With the first category, samples of different classes are mapped into different subspaces, which leads to some redundancy with the class specific base vectors. While for the second category, the samples in each specific class can not be described accurately. In this paper, we first propose a novel class shared dictionary learning method named label embedded dictionary learning (LEDL). It is the improvement based on LCKSVD, which is easier to find out the optimal solution. Then we propose a novel framework named cascaded dictionary learning framework (CDLF) to combine the specific dictionary learning with shared dictionary learning to describe the feature to boost the performance of classification sufficiently. Extensive experimental results on six benchmark datasets illustrate that our methods are capable of achieving superior performance compared to several state-of-art classification algorithms.