Browse Theses

We introduce the use of describable visual attributes for face images. Describable visual attributes are labels that can be given to an image to describe its appearance. This thesis focuses mostly on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: they can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large datasets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images.

We demonstrate the current effectiveness and explore the future potential of using attributes for image search, automatic face replacement in images, and face verification, via both human and computational experiments. To aid other researchers in studying these problems, we introduce two new large face datasets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.

Finally, we also show the effectiveness of visual attributes in a completely different domain: plant species identification. To this end, we have developed and publicly released the Leafsnap system, which has been downloaded by almost half a million users. The mobile phone application is a flexible electronic field guide with high-quality images of the tree species in the Northeast US. It also gives users instant access to our automatic recognition system, greatly simplifying the identification process.