Stable multi-label boosting for image annotation with structural feature selection
Automatic annotating images with appropriate multiple tags are very important to image retrieval and image understanding. We can obtain high-dimensional heterogenous visual features from real-world images to describe their various aspects of visual characteristics, such as color, texture, and shape. Different kinds of heterogenous features have different intrinsic discriminative power for image understanding. The selection of groups of discriminative features for certain semantics is hence crucial to make the image understanding more interpretable. This paper proposes an approach, called stable multi-label boosting with structural feature selection (S-MtBFS), for image annotation. S-MtBFS comprises two steps, namely structural feature selection for each label and stable multi-label boosting by curds and whey. In the first step, a (structural) sparse selection model is learned to identify subgroups of homogenous features for the purpose of predicting a certain label. Moreover, a stable method of multi-label boosting with a re-sampling policy is employed in the second step to utilize the correlations among multiple tags. Extensive experiments on public image datasets show that the proposed approach has better and stable performance of multi-label image annotation and leads to a quite interpretable model for image understanding.
multi-label boosting image annotation structural feature selection stability