Using Language to Learn Structured Appearance Models for Image Annotation

• Published in: IEEE transactions on pattern analysis and machine intelligence 2010-01, Vol.32 (1), p.148-164
• Main Authors: Jamieson, M., Fazly, A., Stevenson, S., Dickinson, S., Wachsmuth, S.
• Format: Article
• Language: English
• Subjects: appearance models; Applied sciences; Artificial intelligence; Assembly; Communications technology; Computer science; control theory; systems; Data processing. List processing. Character string processing; Encoding; Exact sciences and technology; image annotation; Image databases; Image retrieval; Image storage; Language-vision integration; Layout; Memory organisation. Data processing; Object detection; Object recognition; Pattern recognition. Digital image processing. Computational geometry; perceptual grouping; Software; Speech and sound recognition and synthesis. Linguistics; Visual databases
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.283

Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to simultaneously learn the names and appearances of the objects. Only a small fraction of local features within any given image are associated with a particular caption word, and captions may contain irrelevant words not associated with any image object. We propose a novel algorithm that uses the repetition of feature neighborhoods across training images and a measure of correspondence with caption words to learn meaningful feature configurations (representing named objects). We also introduce a graph-based appearance model that captures some of the structure of an object by encoding the spatial relationships among the local visual features. In an iterative procedure, we use language (the words) to drive a perceptual grouping process that assembles an appearance model for a named object. Results of applying our method to three data sets in a variety of conditions demonstrate that, from complex, cluttered, real-world scenes with noisy captions, we can learn both the names and appearances of objects, resulting in a set of models invariant to translation, scale, orientation, occlusion, and minor changes in viewpoint or articulation. These named models, in turn, are used to automatically annotate new, uncaptioned images, thereby facilitating keyword-based image retrieval.