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Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. (English) Zbl 1039.68623
Heyden, Anders (ed.) et al., Computer vision - ECCV 2002. 7th European conference, Copenhagen, Denmark, May 28–31, 2002. Proceedings. Part 4. Berlin: Springer (ISBN 3-540-43748-7). Lect. Notes Comput. Sci. 2353, 97-112 (2002).
Summary: We describe a model of object recognition as machine translation. In this model, recognition is a process of annotating image regions with words. Firstly, images are segmented into regions, which are classified into region types using a variety of features. A mapping between region types and keywords supplied with the images, is then learned, using a method based around EM. This process is analogous with learning a lexicon from an aligned bitext. For the implementation we describe, these words are nouns taken from a large vocabulary. On a large test set, the method can predict numerous words with high accuracy. Simple methods identify words that cannot be predicted well. We show how to cluster words that individually are difficult to predict into clusters that can be predicted well – for example, we cannot predict the distinction between train and locomotive using the current set of features, but we can predict the underlying concept. The method is trained on a substantial collection of images. Extensive experimental results illustrate the strengths and weaknesses of the approach.
For the entire collection see [Zbl 0992.68529].

MSC:
68T45 Machine vision and scene understanding
68T05 Learning and adaptive systems in artificial intelligence
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