干旱区生态环境知识资源中心

In Image classification we classify image into one of the predefined classes. In conventional way, people use different computer vision techniques to extract features from images and different machine learning algorithms use these extracted features to classify the images. It has become very difficult task to classify the images into interpretative classes. Apart from various learning algorithms the accuracy and performance of the model mostly depends on the trained dataset and the algorithm used. In this paper we have proposed a system to classify the scenery images into different groups of sunset, desert, mountains, trees and sea. In this paper the proposed approach for image classification makes essential use of machine learning methods. We focus on deep learning techniques for feature extraction and classification of images. In this paper, we propose a model which does not require creating multiple binary models instead it has single model which predicts the probabilities of different labels and has used these probabilistic threshold values for respective label to convert those probabilities into presence and absence of class/label. This method results into higher accuracy and requires less time as compared to other methods.