Recognition as translating images into text

Kobus Barnard; Pinar Duygulu; David A. Forsyth

doi:10.1117/12.478427

10 January 2003 Recognition as Translating Images into Text

Kobus Barnard, Pinar Duygulu, David A. Forsyth

Proceedings Volume 5018, Internet Imaging IV; (2003) https://doi.org/10.1117/12.478427
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

We present an overview of a new paradigm for tackling long standing computer vision problems. Specifically our approach is to build statistical models which translate from a visual representations (images) to semantic ones (associated text). As providing optimal text for training is difficult at best, we propose working with whatever associated text is available in large quantities. Examples include large image collections with keywords, museum image collections with descriptive text, news photos, and images on the web. In this paper we discuss how the translation approach can give a handle on difficult questions such as: What counts as an object? Which objects are easy to recognize and which are hard? Which objects are indistinguishable using our features? How to integrate low level vision processes such as feature based segmentation, with high level processes such as grouping. We also summarize some of the models proposed for translating from visual information to text, and some of the methods used to evaluate their performance.

Citation Download Citation

Kobus Barnard, Pinar Duygulu, and David A. Forsyth "Recognition as Translating Images into Text", Proc. SPIE 5018, Internet Imaging IV, (10 January 2003); https://doi.org/10.1117/12.478427

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