CLASSIFICATION OF DIGITAL IMAGES USING FUSION ELEVATED ORDER CLASSIFIER IN WAVELET NEURAL NETWORK
R. Arulmurugan and P. Sengottuvelan
DOI : 10.3844/jcssp.2014.1827.1838
Journal of Computer Science
Volume 10, Issue 9
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.
© 2014 R. Arulmurugan and P. Sengottuvelan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.