WEB IMAGE INTERPRETATION: SEMI-SUPERVISED MINING ANNOTATED WORDS

An image is worth of thousand words. Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques are very difficult to get natural language interpretation for images such as "pandas eat bamboo". In this paper, we proposed an approach to interpret image semantics through semi-supervised mining annotated words. The idea in this approach mainly consists of three parts: at first, the visibility of annotated words of target image is calculated by semi-supervised learning approach from the landmark words in WordNet; then the annotated words are used as queries to retrieve matched web pages; at last, the meaningful sentences in the matched web pages are ranked as the interpretation of target image by semi-supervised learning approach. Experiments conducted on real-world web images demonstrate the effectiveness of the proposed approach.