Biomedical imaging modality classification using combined visual features and textual terms
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
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Image category recognition is important to access visual information on the level of objects and scene types. This paper presents a new algorithm for the automatic recognition of object and scene classes. Compact and yet discriminative visual-words and low-level-features object class subspaces are automatically learned from a set of training images by a Supervised Nonlinear Neighborhood Embedding (SNNE) algorithm, which can learn an adaptive nonlinear subspace by preserving the neighborhood structure of the visual feature space. The main contribution of this paper is two fold: i) an optimally compact and discriminative feature subspace is learned by the proposed SNNE algorithm for different feature space (visual-word and low-level features). ii) An effective merge of different feature subspace can be implemented simply. High classification accuracy is demonstrated on different database including the scene databas (Simplicity) and object recognition database (Caltech). We confirm that the proposed strategy is much better than state-of-the-art methods for different databases.