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

Authors:
Dmitriy Bespalov;Anders Lindbjerg Dahl;Bing Bai;Ali Shokoufandeh
Affiliations:
Department of Computer Science, Drexel University;DTU Informatics, Technical University of Denmark;NEC Labs America;Department of Computer Science, Drexel University
Venue:
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Year:
2011

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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.