Multi-label boosting for image annotation by structural grouping sparsity

We can obtain high-dimensional heterogenous features from real-world images to describe their various aspects of visual characteristics, such as color, texture and shape etc.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 formulates the multi-label image annotation as a regression model with a regularized penalty. We call it Multi-label Boosting by the selection of heterogeneous features with structural Grouping Sparsity (MtBGS). MtBGS induces a (structural ) sparse selection model to identify subgroups of homogenous features for predicting a certain label. Moreover, the correlations among multiple tags are utilized in MtBGS to boost the performance of multi-label annotation. Extensive experiments on public image datasets show that the proposed approach has better multi-label image annotation performance and leads to a quite interpretable model for image understanding.

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