Integrating hierarchical feature selection and classifier training for multi-label image annotation

It is well accepted that using high-dimensional multi-modal visual features for image content representation and classifier training may achieve more sufficient characterization of the diverse visual properties of the images and further result in higher discrimination power of the classifiers. However, training the classifiers in a high-dimensional multi-modal feature space requires a large number of labeled training images, which will further result in the problem of curse of dimensionality. To tackle this problem, a hierarchical feature subset selection algorithm is proposed to enable more accurate image classification, where the processes for feature selection and classifier training are seamlessly integrated in a single framework. First, a feature hierarchy (i.e., concept tree for automatic feature space partition and organization) is used to automatically partition high-dimensional heterogeneous multi-modal visual features into multiple low-dimensional homogeneous single-modal feature subsets according to their certain physical meanings and each of them is used to characterize one certain type of the diverse visual properties of the images. Second, principal component analysis (PCA) is performed on each homogeneous singlemodal feature subset to select the most representative feature dimensions and a weak classifier is learned simultaneously. After the weak classifiers and their representative feature dimensions are available for all these homogeneous single-modal feature subsets, they are combined to generate an ensemble image classifier and achieve hierarchical feature subset selection. Our experiments on a specific domain of natural images have also obtained very positive results.