  There is growing interest in multi-label image classification due to its
critical role in web-based image analytics-based applications, such as
large-scale image retrieval and browsing. Matrix completion has recently been
introduced as a method for transductive (semi-supervised) multi-label
classification, and has several distinct advantages, including robustness to
missing data and background noise in both feature and label space. However, it
is limited by only considering data represented by a single-view feature, which
cannot precisely characterize images containing several semantic concepts. To
utilize multiple features taken from different views, we have to concatenate
the different features as a long vector. But this concatenation is prone to
over-fitting and often leads to very high time complexity in MC based image
classification. Therefore, we propose to weightedly combine the MC outputs of
different views, and present the multi-view matrix completion (MVMC) framework
for transductive multi-label image classification. To learn the view
combination weights effectively, we apply a cross validation strategy on the
labeled set. In the learning process, we adopt the average precision (AP) loss,
which is particular suitable for multi-label image classification. A least
squares loss formulation is also presented for the sake of efficiency, and the
robustness of the algorithm based on the AP loss compared with the other losses
is investigated. Experimental evaluation on two real world datasets (PASCAL
VOC' 07 and MIR Flickr) demonstrate the effectiveness of MVMC for transductive
(semi-supervised) multi-label image classification, and show that MVMC can
exploit complementary properties of different features and output-consistent
labels for improved multi-label image classification.
