Recently, image categorization has been an active research topic due to the urgent
need to retrieve and browse digital images via semantic keywords. This paper formulates image categorization as a multi-label classification problem using recent
advances in matrix completion. Under this setting, classification of testing data
is posed as a problem of completing unknown label entries on a data matrix that
concatenates training and testing features with training labels. We propose two
convex algorithms for matrix completion based on a Rank Minimization criterion
specifically tailored to visual data, and prove its convergence properties. A major
advantage of our approach w.r.t. standard discriminative classification methods
for image categorization is its robustness to outliers, background noise and partial occlusions both in the feature and label space. Experimental validation on
several datasets shows how our method outperforms state-of-the-art algorithms,
while effectively capturing semantic concepts of classes.
