A New Large-scale Image Automatic Annotation System based on WordNet

Automatic image annotation is very important for image retrieval. Despite continuous efforts in inventing new annotation algorithms, the annotation performance is usually unsatisfactory, and the annotation vocabulary is still limited due to the use of a small scale training set. In this paper a novel image automatic annotation system based on the WordNet is presented, named WordNet-based image annotation. By using WordMet hierarchical structure, we collect a large image datasets. And each image is loosely labeled with one of the non-abstract nouns in English, as listed in the WordNet lexical database. Then we use PageRank method to delete the wrong images under every word, and make sure that every word covers 100 images. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from WordNet can be used in conjunction with SVM classifiers to perform object classification over a range of semantic levels minimizing the effects of labeling noise. The system models a real-world situation by including pictures gathered from the Internet and is designed for exploratory large scale image retrieval system based on the internet.