Learning efficient structured dictionary for image classification

Abstract

Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification. In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of dictionary learning. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches which impose computationally expensive 1 -norm constraint on the coefficient matrix, ESDL employs 2 -norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks. The demo code of our proposed ESDL will be available at https://github.com/li-zi-qi/ESDL.

Multidomain feature fusion method for small object classification: MDFF

“…3 Convolutional neural networks (CNNs) generally use methods such as network deepening and feature multiplexing to enhance the classifier's ability to extract spatial features from the target. 4,5 ResNet 6 uses the idea of residual learning. On the basis of VGG19, 7 a residual unit is added through a shortcut to solve the degradation problem of the deep network so that the network becomes deeper and can extract deeper features.…”

“…Deep learning methods have achieved impressive results on regular-sized targets 3 . Convolutional neural networks (CNNs) generally use methods such as network deepening and feature multiplexing to enhance the classifier’s ability to extract spatial features from the target 4 , 5 . ResNet 6 uses the idea of residual learning.…”