On inferring image label information using rank minimization for supervised concept embedding

Abstract

Concept-based representation - combined with some classifier (e.g., support vector machine) or regression analysis (e.g., linear regression) - induces a popular approach among image processing community, used to infer image labels. We propose a supervised learning procedure to obtain an embedding to a latent concept space with the pre-defined inner product. This learning procedure uses rank minimization of the sought inner product matrix, defined in the original concept space, to find an embedding to a new low dimensional space. The empirical evidence show that the proposed supervised learning method can be used in combination with another computational image embedding procedure, such as bag-of-features method, to significantly improve accuracy of label inference, while producing embedding of low complexity. © 2011 Springer-Verlag.