Boosting-based Automatic Linguistic Indexing of Pictures
基于Boosting学习的图片自动语义标注

Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in content-based image retrieval.In this paper,a boosting-based automatic linguistic indexing approach is proposed and a linguistic indexing system called BLIR(Boosting for Linguistic indexing Image Retrieval system) is built.It is assumed that images of same semantic meaning can be represented by a model combined with a group of features.2D-MHMM model is found to be such a template for one special kind of color and texture combination,which corresponds to one cluster in feature space.Thus in BLIR system, a large number of 2D-MHMM models are generated and a boosting algorithm is used to associate keyword with models.The system has been implemented and tested on a photographic image database of about(60 000) images.Results demonstrate the effectiveness of the proposed technique which outperforms other approaches.