Learning Structured Appearance Models from Captioned Images of Cluttered Scenes

Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to learn both the names and appearances of the objects. Only a small number of local features within any given image are associated with a particular caption word. We describe a connected graph appearance model where vertices represent local features and edges encode spatial relationships. We use the repetition of feature neighborhoods across training images and a measure of correspondence with caption words to guide the search for meaningful feature configurations. We demonstrate improved results on a dataset to which an unstructured object model was previously applied. We also apply the new method to a more challenging collection of captioned images from the Web, detecting and annotating objects within highly cluttered realistic scenes.