Abstract
The revival of wavelet neural networks obtained an extensive use in digital image processing. The shape representation, classification and detection play a very important role in the image analysis. Boosted Greedy Sparse Linear Discriminate Analysis (BGSLDA) trains the cascade level of detection in an efficient manner. With the application of reweighting concept and deployment of class-reparability criterion, lesser search was made on more efficient weak classifiers. At the same time, Multi-Scale Histogram of Oriented Gradients (MS-HOG) method removes the confined portions of images. MS-HOG algorithm includes the advanced recognition scenarios such as rotations transportations on multiple objects but does not perform effective feature classification. To overcome the drawbacks in classification of higher order units, Fusion Elevated Order Classifier (FEOC) method is introduced. FEOC contains a different fusion of high order units to deal with diverse datasets by making changes in the order of units with parametric considerations. FEOC uses a prominent value of input neurons for better fitting properties resulting in a higher level of learning parameters (i.e.,) weights. FEOC method features are reduced using feature subset collection method. However, elevation mechanisms are significantly applied to the neuron, neuron activation function type and finally in the higher order types of neural network with the functions of adaptive in nature. FEOC have evaluated sigma-pi network representing both the Elevated order Processing Unit (EPU) and pi-sigma network. The experimental performance of Fusion Elevated Order Classifier in the wavelet neural network is evaluated against BGSLDA and MS-HOG using Statlog (Landsat Satellite) Data Set from UCI repository. FEOC performed in MATLAB with factors such as classification accuracy rate, false positive error, computational cost, memory consumption, response time and higher order classifier rate.
Highlights
The theory of wavelets is useful in recognizing the prepared on applications of WNN, which combine the common properties for approximation for Wavelet capability of artificial neural networks
The experimental performance of Fusion Elevated Order Classifier in the wavelet neural network is evaluated against Boosted Greedy Sparse Linear Discriminate Analysis (BGSLDA) and Multi-Scale Histogram of Oriented Gradients (MS-HOG) using Statlog (Landsat Satellite) Data Set from UCI repository
Fusion Elevated Order Classifier (FEOC) method on wavelet neural network is experimented on MATLAB code
Summary
Designed using the two important properties of wavelets namely,time and frequency. Much research has been. Neural Networks (WNN) and initialization for the learning process and the capability of wavelet heuristics for rapid training. Cortex-based ANN models are illustrated to end with and discuss about the throughout Another form of neural network, Convolutional Neural Networks (CNN) is trained to perform dynamic state features in (Shruthi, 2011). Feature selection as demonstrated in (Alexander and Vladimir, 2012) analyzed the relationship of scores obtained in the initial learning process for classifying of objects using neural network classifiers. Existing image storing systems limit classification mechanism to explain an image based on the information, quality, or shape features on WNN.
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