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Classifier Integration Model for Image Classification  

Park, Dong-Chul (Dept. of Electronics Eng., Myong Ji University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.49, no.2, 2012 , pp. 96-102 More about this Journal
Abstract
An advanced form of the Partitioned Feature-based Classifier with Expertise Table(PFC-ET) is proposed in this paper. As is the case with the PFC-ET, the proposed classifier model, called Classifier Integration Model(CIM), does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. The proposed CIM utilizes a proportion of selected cluster members instead of the expertise table in PFC-ET to minimize the error in confusion table. The proposed CIM is applied to the classification problem on two data sets, Caltech data set and collected terrain data sets. When compared with PFC model and PFC-ET model. the proposed CIM shows improvements in terms of classification accuracy and post processing efforts.
Keywords
classifier; image classification; clustering; neural networks; feature vector;
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Times Cited By KSCI : 1  (Citation Analysis)
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