Title :
A comparative study of ANN, k-means and AdaBoost algorithms for image classification
Author :
Periyasamy, N. ; Thamilselvan, P. ; Sathiaseelan, J.G.R.
Author_Institution :
Dept. of CS, Bishop Heber Coll., Tiruchirappalli, India
Abstract :
Data mining is the method of extracting the valuable systematic information from huge databases. Image classification has constantly been a vital task for several applications such as remote sensing medical field, pattern recognition. It converses to the task of removing information classes from a multiband raster image. The resolving of the classification method is to classify all pixels in a one image class into another class. The target of image classification is to find the exclusive dark level of images. This paper concentrates on the study of artificial neural network, Adaboost and k-means algorithms in image classification.
Keywords :
image classification; learning (artificial intelligence); neural nets; ANN; AdaBoost algorithm; artificial neural network; data mining; image classification; k-means algorithm; multiband raster image; Accuracy; Algorithm design and analysis; Artificial neural networks; Classification algorithms; Data mining; Image classification; Support vector machines; AdaBoost; Artificial Neural Network; Classification Accuracy; Data Mining; Image Classification; K-Means;
Conference_Titel :
Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-6817-6
DOI :
10.1109/ICIIECS.2015.7193067
