Even a relatively unstructured captioned image set depicting a variety of objects in cluttered scenes contains strong correlations between caption words and repeated visual structures. We exploit these correlations to discover named objects and learn hierarchical models of their appearance. Revising and extending a previous technique for finding small, distinctive configurations of local features, our method assembles these co-occurring parts into graphs with greater spatial extent and flexibility. The resulting multipart appearance models remain scale, translation and rotation invariant, but are more reliable detectors and provide better localization. We demonstrate improved annotation precision and recall on datasets to which the non-hierarchical technique was previously applied and show extended spatial coverage of detected objects. © 2010 Springer-Verlag.
CITATION STYLE
Jamieson, M., Eskin, Y., Fazly, A., Stevenson, S., & Dickinson, S. (2010). Discovering multipart appearance models from captioned images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6315 LNCS, pp. 183–196). Springer Verlag. https://doi.org/10.1007/978-3-642-15555-0_14
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