Optimal Tag Sets for Automatic Image Annotation

In this paper we introduce a new form of the Continuous Relevance Model (the BSCRM) that captures the correlation between tags in a formal and consistent manner. We apply a beam search algorithm to find a near optimal set of mutually correlated tags for an image in a time that is linear in the depth of the search tree. We conduct an examination of the model performance under different kernels for the representation of the image feature distributions and suggest a method of adapting the kernel to the dataset. BS-CRM with a Minkowski kernel is found to significantly increase recall by 42% and precision by 38% over the original CRM model and outperforms more recent baselines on the standard Corel 5k dataset.