A syncretic representation for image classification and face recognition

AbstractFor representation based image classification methods, it is very important to well represent the target image. As pixels at same positions of training samples and test samples of an object usually have different intensities, it brings difficulty in correctly classifying the object. In this paper, we proposed a novel method to reduce the effects of this issue for image classification. Our method first produces a new representation (i.e. virtual image) of original image, which can enhance the importance of moderate pixel intensities and reduce the effects of larger or smaller pixel intensities. Then virtual images and corresponding original images are respectively used to represent a test sample and obtain two representation results. Finally, this method fuses these two results to classify the test sample. The integration of original image and its virtual image is able to improve the accuracy of image classification. The experiments of image classification show that the proposed method can obtain a higher accuracy than the conventional classification methods

Exploring issues of balanced versus imbalanced samples in mapping grass community in the telperion reserve using high resolution images and selected machine learning algorithms

ABSTRACT Accurate vegetation mapping is essential for a number of reasons, one of which is for conservation purposes. The main objective of this research was to map different grass communities in the game reserve using RapidEye and Sentinel-2 MSI images and machine learning classifiers [support vector machine (SVM) and Random forest (RF)] to test the impacts of balanced and imbalance training data on the performance and the accuracy of Support Vector Machine and Random forest in mapping the grass communities and test the sensitivities of pixel resolution to balanced and imbalance training data in image classification. The imbalanced and balanced data sets were obtained through field data collection. The results show RF and SVM are producing a high overall accuracy for Sentinel-2 imagery for both the balanced and imbalanced data set. The RF classifier has yielded an overall accuracy of 79.45% and kappa of 74.38% and an overall accuracy of 76.19% and kappa of 73.21% using imbalanced and balanced training data respectively. The SVM classifier yielded an overall accuracy of 82.54% and kappa of 80.36% and an overall accuracy of 82.21% and a kappa of 78.33% using imbalanced and balanced training data respectively. For the RapidEye imagery, RF and SVM algorithm produced overall accuracy affected by a balanced data set leading to reduced accuracy. The RF algorithm had an overall accuracy that dropped by 6% (from 63.24% to 57.94%) while the SVM dropped by 7% (from 57.31% to 50.79%). The results thereby show that the imbalanced data set is a better option when looking at the image classification of vegetation species than the balanced data set. The study recommends the implementation of ways of handling misclassification among the different grass species to improve classification for future research. Further research can be carried out on other types of high resolution multispectral imagery using different advanced algorithms on different training size samples.EM201