Boosting image classification scheme

Image classification is very active and promising research domain in image retrieval and management. We propose a boosting image classification scheme with automatic selection of discriminative features. Firstly, we present an image feature called the orientational color correlogram (OCC) and apply it to image classification. OCC extends the color correlogram by adding in orientational information which can take into account both the local color correlation and the global context structure of an image. Secondly, we give a solution to feature selection for the very high dimensionality of OCC by using a boosting classification scheme which can select the most discriminative features automatically. In our experiments, only a small number of elements of OCC are selected, which can reduce the storage space of classifier models and speed up the classification process. The experimental results suggest the proposed method has preferable performances