High-level, part-based features for fine-grained visual categorization

Object recognition--"What is in this image?"--is one of the basic problems of computer vision. Most work in this area has been on finding basic-level object categories such as plant, car, and bird, but recently there has been an increasing amount of work in fine-grained visual categorization, in which the task is to recognize subcategories of a basic-level category, such as blue jay and bluebird. Experimental psychology has found that while basic-level categories are distinguished by the presence or absence of parts (a bird has a beak but car does not), subcategories are more often distinguished by the characteristics of their parts (a starling has a narrow, yellow beak while a cardinal has a wide, red beak). In this thesis we tackle fine-grained visual categorization, guided by this observation. We develop alignment procedures that let us compare corresponding parts, build classifiers tailored to finding the interclass differences at each part, and then combine the per-part classifiers to build subcategory classifiers. Using this approach, we outperform previous work in several fine-grained categorization settings: bird species identification, face recognition, and face attribute classification. In addition, the construction of subcategory classifiers from part classifiers allows us to automatically determine which parts are most relevant when distinguishing between any two subcategories. We can use this to generate illustrations of the differences between subcategories. To demonstrate this, we have built a digital field guide to North American birds which includes automatically generated images highlighting the key differences between visually similar species. This guide, "Birdsnap," also identifies bird species in users' uploaded photos using our subcategory classifiers. We have released Birdsnap as a web site and iPhone application