Survey on Supervised Classification using Self Organising Maps
نویسندگان
چکیده
Image classification is an important topic in digital image processing, and it could be solved by pattern recognition methods. This paper is a survey based on Self Organising Maps used as a supervised algorithm for image classification. It is observed that SOM can be used as a supervised method, and can have better advantages: better predictions, easier to interpret and better stability. Keywords— SOM, SOM-KS, hit map, saliency map, colour space , entropy index.
منابع مشابه
Self-Organising Maps in Document Classification: A Comparison with Six Machine Learning Methods
This paper focuses on the use of self-organising maps, also known as Kohonen maps, for the classification task of text documents. The aim is to effectively and automatically classify documents to separate classes based on their topics. The classification with self-organising map was tested with three data sets and the results were then compared to those of six well known baseline methods: k-mea...
متن کاملBagging and Bumping Self Organising Maps
In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to c...
متن کاملDetermination of Best Supervised Classification Algorithm for Land Use Maps using Satellite Images (Case Study: Baft, Kerman Province, Iran)
According to the fundamental goal of remote sensing technology, the image classification of desired sensors can be introduced as the most important part of satellite image interpretation. There exist various algorithms in relation to the supervised land use classification that the most pertinent one should be determined. Therefore, this study has been conducted to determine the best and most su...
متن کاملThe automated classification of astronomical lightcurves using Kohonen self-organising maps
We apply the technique of self-organising maps (Kohonen 1990) to the automated classification of singly periodic astronomical lightcurves. We find that our maps readily distinguish between lightcurve types in both synthetic and real datasets, and that the resulting maps do not depend sensitively on the chosen learning parameters. Automated data analysis techniques are likely to be become increa...
متن کاملAnalytic Comparison of Audio Feature Sets using Self-Organising Maps
A wealth of different feature sets for analysing music has been proposed and employed in several different Music Information Retrieval applications. In many cases, the feature sets are compared with each other based on benchmarks in supervised machine learning, such as automatic genre classification. While this approach makes features comparable for specific tasks, it doesn’t reveal much detail...
متن کامل