Dynamic Mode Decomposition based feature for Image Classification

TL;DR

This paper introduces a novel feature extraction method using Dynamic Mode Decomposition (DMD) for image classification, demonstrating competitive results with existing approaches on Imagenet data.

Contribution

It proposes a new DMD-based feature extraction technique for image classification, especially useful for unlabeled data, and evaluates its effectiveness with various classifiers.

Findings

DMD features with RKS achieve competitive accuracy

The method performs well on Imagenet data

DMD can extract meaningful features from unlabeled data

Abstract

Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.