IEEE Trans Pattern Anal Mach Intell - Describable Visual Attributes for Face Verification and Image Search.

Tópicos

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Resumo

We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses 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 data sets 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 face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.

Resumo Limpo

introduc use describ visual attribut face verif imag search describ visual attribut label can given imag describ appear paper focus imag face attribut use describ although concept also appli domain exampl face attribut includ gender age jaw shape nose size etc advantag attributebas represent vision task manifold can compos creat descript various level specif generaliz can learn appli recogn new object categori without train effici possibl requir exponenti fewer attribut train data explicit name categori show one can creat label larg data set realworld imag train classifi measur presenc absenc degre attribut express imag classifi can automat label new imag demonstr current effectivenessand explor futur potentialof use attribut face verif imag search via human comput experi final introduc two new face data set name facetrac pubfig label attribut ident respect

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