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

Image classification is play an important role in s ecurity surveillance in current scenario of huge Am ount of image data base. Due to rapid change of feature con tent of image are major issues in classification. T he image classification is improved by various authors using different model of classifier. The efficiency of c lassifier model depends on feature extraction process of traffic im age. For the feature extraction process various aut hors used a different technique such as Gabor feature extractio n, histogram and many more method on extraction process for classification. We apply the FLDA-GA for improved the classification rate of content based image clas sification. The improved method used heuristic function genetic algorithm. In the form of optimal GA used as feature o ptimizer for FLDA classification. The normal FLDA suffered from a problem of core and outlier problem. The both sid e kernel technique improved the classification process of su pport vector machine.FLDA perform a better classifi cation in compression of another binary multi-class classific ation. Directed acyclic graph applied a graph porti on technique for the mapping of feature data. The mapping space of f eature data mapped correctly automatically improved the voting process of classification.