چکیده (انگلیسی):

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.