multi-progressive model for web image annotation
Web image annotation is a very important method to effectively index and search images on the internet. Many web image annotation approaches utilized not only the visual information but also the texts emerged with web image. However, they failed to utilize the whole relations among annotations, which reflect the specific semantic content of images. In this paper we propose a novel Multi-Progressive Model (MPM) for web image annotation that leverages word correlations between available texts of web images and an automatic-built vocabulary. The proposed approach treat the available text of web images as initial annotations, and extend them by using a pre-defined lexicon to include more words which are potentially relevant to the target image. It then rank initial and extended annotations by taking advantage of whole words relations without bringing huge computation. The multi-progressive model can be viewed as a greedy optimization algorithm that approximately optimizes the joint annotation probability in a progressive way. Experimental results on web images demonstrate the effectiveness of the proposed model.
multi-progressive model web image annotation