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Neeraj Kumar - Describable Visual Attributes for Face Search and Recognition [1 of 4]

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Published on Jun 17, 2011

Talk given at UNC-Chapel Hill by Neeraj Kumar, PhD candidate at Columbia University, on May 25, 2011.

Please see my website at http://neerajkumar.org/ for more details about my projects!

Abstract:
Describable visual attributes are labels that can be given to an image to describe its appearance. For example, faces can be described using the attributes "gender", "age", or "jaw shape", while leaves can be described as "compound", "serrated", "lobed", 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 effectiveness of using attributes for search, recognition, part localization, automatic image editing, and more. This talk focuses on images of faces and the attributes used to describe them, but shows how the concepts can be applied to other domains as well.

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