In the development of an image segmentation system for real time image processing applications, we apply the classical decision analysis paradigm by viewing image segmentation as a pixel classifica.tion task. We use supervised training to derive a classifier for our system from a set of examples of a particular pixel classification problem. In this study, we test the suitability of a connection...

An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...

We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training image. Then, using a simple diagonal illumination model, the illuminants in a new scene that contains some of the same surface classes are estimated in a Maximum Likelihood framework using the Expectation Maximization a...

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation, Adaptive resonance theory (ART) neural networks offer advan...

This study describes the parcel-based classification of agricultural crops using multi-date Landsat 7 ETM+ images acquired in May, July, and August 2000. The study area is located in North-West of Turkey with an area of about 170 km and grows a variety of crops. The objective was to map the summer (August) crops within the agricultural land parcels. The classification methodology is based on a ...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...

One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...