Classification of the Chinese Handwritten Numbers with Supervised Projective Dictionary Pair Learning

by   Rasool Ameri, et al.

Image classification has become a key ingredient in the field of computer vision. To enhance classification accuracy, current approaches heavily focus on increasing network depth and width, e.g., inception modules, at the cost of computational requirements. To mitigate this problem, in this paper a novel dictionary learning method is proposed and tested with Chinese handwritten numbers. We have considered three important characteristics to design the dictionary: discriminability, sparsity, and classification error. We formulated these metrics into a unified cost function. The proposed architecture i) obtains an efficient sparse code in a novel feature space without relying on ℓ_0 and ℓ_1 norms minimisation; and ii) includes the classification error within the cost function as an extra constraint. Experimental results show that the proposed method provides superior classification performance compared to recent dictionary learning methods. With a classification accuracy of ∼98%, the results suggest that our proposed sparse learning algorithm achieves comparable performance to existing well-known deep learning methods, e.g., SqueezeNet, GoogLeNet and MobileNetV2, but with a fraction of parameters.


page 1

page 2

page 3

page 4

page 5

page 6


Class Specific or Shared? A Hybrid Dictionary Learning Network for Image Classification

Dictionary learning methods can be split into two categories: i) class s...

Semi-supervised dictionary learning with graph regularization and active points

Supervised Dictionary Learning has gained much interest in the recent de...

A multi-class structured dictionary learning method using discriminant atom selection

In the last decade, traditional dictionary learning methods have been su...

Label Embedded Dictionary Learning for Image Classification

Recently, label consistent k-svd(LC-KSVD) algorithm has been successfull...

Supervised Dictionary Learning

It is now well established that sparse signal models are well suited to ...

Please sign up or login with your details

Forgot password? Click here to reset