Supervised learning of semantic classes for image annotation and retrieval

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Supervised learning of semantic classes for image annotation and retrieval (2007)

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by Gustavo Carneiro , Antoni B. Chan , Pedro J. Moreno , Nuno Vasconcelos

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	223 - 18 self

BibTeX

@ARTICLE{Carneiro07supervisedlearning,
    author = {Gustavo Carneiro and Antoni B. Chan and Pedro J. Moreno and Nuno Vasconcelos},
    title = {Supervised learning of semantic classes for image annotation and retrieval},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {2007},
    volume = {29},
    pages = {2007}
}

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Abstract

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. Index Terms—Content-based image retrieval, semantic image annotation and retrieval, weakly supervised learning, multiple instance learning, Gaussian mixtures, expectation-maximization, image segmentation, object recognition. 1

Keyphrases

semantic class image annotation semantic image annotation supervised formulation semantic label common semantic label density estimate image segmentation gaussian mixture minimum probability training image theoretical argument classification problem prior semantic segmentation probabilistic formulation error annotation database image extensive experiment multiple instance learning localized feature vector mixture density one-to-one correspondence computational cost joint modeling corresponding semantic class object recognition multiple instance learning argument hierarchical extension index term content-based image retrieval visual feature distribution

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