We describe an efficient and scalable system for automatic image categorization. Our approach seeks to marry scalable “model-free” neighborhood-based annotation with accurate boosting-based per-tag modeling. For accelerated neighborhood-based classification, we use a set of spatial data structures as weak classifiers for an arbitrary number of categories. We employ standard edge and color features and an approximation scheme that scales to large training sets. The weak classifier outputs are combined in a tag-dependent fashion via boosting to improve accuracy. The method performs competitively with standard SVM-based per-tag classification with substantially reduced computational requirements. We present multi-label image annotation experiments using data sets of more than two million photos.