The Community for Technology Leaders
Green Image
Issue No. 03 - March (2007 vol. 29)
ISSN: 0162-8828
pp: 394-410
Gustavo Carneiro , Integrated Data Systems Department, Siemens Corporate Research, Princeton, NJ
Antoni B. Chan , Department of Computer and Electrical Engineering, University of California, San Diego, La Jolla, CA
Pedro J. Moreno , Google Inc., New York, NY
Nuno Vasconcelos , Department of Computer and Electrical Engineering, University of California, San Diego, La Jolla, CA
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
Supervised learning, Image retrieval, Image databases, Information retrieval, Image segmentation, Labeling, Unsupervised learning, Visual databases, Computational efficiency, Robustness

G. Carneiro, A. B. Chan, P. J. Moreno and N. Vasconcelos, "Supervised Learning of Semantic Classes for Image Annotation and Retrieval," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. 3, pp. 394-410, 2007.
84 ms
(Ver 3.3 (11022016))