Operations Research, Systems Engineering and Industrial Engineering Commons^™

Semantic Classification Of Rural And Urban Images Using Learning Vector Quantization, Prakash Thulasiraman

LSU Master's Theses

One of the major hurdles in semantic image classification is that only low-level features can be reliably extracted from images as opposed to higher level features (objects present in the scene and their inter-relationships). The main challenge lies in grouping images into semantically meaningful categories based on the available low-level visual features of the images. It is important that we have a classification method that will handle a complex image dataset with not so well defined boundaries between clusters. Learning Vector Quantization (LVQ) neural networks offer a great deal of robustness in clustering complex datasets. This study presents a semantic …