Image Annotation Using The Machine Learning Algorithms

"Semantic Clarity" is an important prerequisite of a large-scale digital image management, it exists a big gap between the underlying features of the image and advanced semantics of the image understood by human. Therefore, automatic acquisition of the semantic content of the image through computer information technology is very important theoretical and practical significance. The substance of automatic image annotation is to obtain high-level semantic keywords through processing and analyzing the underlying visual information features of image. We use this set of top semantic keywords to represent the image features in the same way which image can be retrieved as current text search. Automatic image annotation based on classification is one of the most widely used methods in the current image annotation fields.The research goal is to combine the characteristics of the current annotation model, and use machine learning classification algorithm to annotate the image. Compared with the previous classification based on the classic model of automatic image annotation algorithm, the proposed decision tree algorithm classification has a high improvement in accuracy, and the system can use rules to mark the image that can be understood. In order to obtain the labeling rules, we must first carry out the training process of the whole system. After each image on the training set are segmented, we have all regions of a certain similarity, then extract the visual features of each region, finally train on the segmented regions using machine learning algorithm. In this paper, the main concern is the improved NewNBtree algorithm based on the classical algorithm, SimpleC4.5 algorithm and FastRandomForest algorithm training. The appropriate decision rules can be obtained through the training, and ultimately automatic semantic annotation can be realized. In the stage of the automatic semantic annotation, we use the concept of information entropy of image to exclude the noisy region, which in turn more effectively can improve the annotation system in accuracy.In this paper, experiments are performed to verify the effectiveness and robustness of the algorithms and system with a standard Corel image library. It includes 10 different data sets based on Corel image database. The experimental results shows that the proposed algorithm is better than the traditional decision tree learning algorithm for classification of image data and is effectively applied to large-scale training image sets. At last, automatic image annotation system can be implemented based on the machine learning algorithms.