Performance Analysis of Image Classification Algorithm Based on Feature Fusing Technique Model

Unclassified region deceases the efficiency and performance of PLSA and FLDA . The proper selection of feature sub set reduced the unclassified region of PLSA and FLDA . Now a day?s binary classification are widely used in image classification. The mapping of data one space to another space creates diversity of outlier and noise and generate unclassified region for image classification. For the reduction of unclassified region we used radial basis function for sampling of feature and reduce the noise and ou tlier for feature space of data and increase the performance and efficiency of image classification. Our proposed method optimized the feature selection process and finally sends data to FLDA classifier for classification of data. Here we used fisher class ifier . As a classifier FLDA suffering two problems (1) how to choose optimal feature sub set input and (2) how to set best kernel parameters. These problems influence the performance and accuracy of FLDA . Now the pre - sampling of feature reduced the feature selection process of FLDA for image classification