10-04-2017, 07:48 PM
Abstract A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as
classification problems where each class is defined as the group of database images labeled with a common semantic label. It is
shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of
error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not
require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a
mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label
pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument
and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the
more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical
arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously
published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter
tuning.