Feature Extraction Process is in the classification is domain adaptive genetic features methodology. By using this algorithm from to classify the relative images for consecutive input image in the trained database. So, to checks the fitness criteria from the input image to classify the output image as solution for the features descriptors of the input image. Visual analytics is a human-machine collaboration to data modeling where extraction of the most informative features plays an important role. Although feature extraction is a multi-objective task, the traditional algorithms either only consider one objective or aggregate the objectives into one scalar criterion to optimize. In this paper, we propose a Pareto-based multi-objective approach to feature extraction for visual analytics applied to data classification problems. We identify classifiability, visual interpretability and semantic interpretability as the three equally important objectives for feature extraction in classification problems and define various measures to quantify these objectives. Our results on a number of benchmark datasets show consistent improvement compared to three standard dimensionality reduction techniques. We also argue that exploration of the multiple Pareto-optimal models provide more insight about the classification problem as opposed to a single optimal solution. IMAGE classification has become an active research field in computer vision due to its wide range of applications image search and retrieval object detection and recognition and even human-computer interaction The basic image classification algorithm is generally introduced in and involves two main stages: low-level feature extraction and representation and Feature Learning for Image Classification via Multi objective Genetic Programming high level image classification.