Review of Image Classification Techniques Based on LDA, PCA and Genetic Algorithm

Image classification is play an important role in security surveillance in current scenario of huge Amount of image data base. Due to rapid change of feature content of image are major issues in classification. The image classification is improved by various authors using different model of classifier. The efficiency of classifier model depends on feature extraction process of traffic image. For the feature extraction process various authors used a different technique such as Gabor feature extraction, histogram and many more method on extraction process for classification. We apply the FLDA-GA for improved the classification rate of content based image classification. The improved method used heuristic function genetic algorithm. In the form of optimal GA used as feature optimizer for FLDA classification. The normal FLDA suffered from a problem of core and outlier problem. The both side kernel technique improved the classification process of support vector machine.FLDA perform a better classification in compression of another binary multi-class classification. Directed acyclic graph applied a graph portion technique for the mapping of feature data. The mapping space of feature data mapped correctly automatically improved the voting process of classification.