Supervised Learning of Semantic Classes for Image Annotation and Retrieval

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

789 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)394-410
Journal / PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number3
Publication statusPublished - Mar 2007
Externally publishedYes

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.

Research Area(s)

  • Content-based image retrieval, Expectation-maximization, Gaussian mixtures, Image segmentation, Multiple instance learning, Object recognition, Semantic image annotation and retrieval, Weakly supervised learning

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