  It is critical and meaningful to make image classification since it can help
human in image retrieval and recognition, object detection, etc. In this paper,
three-sides efforts are made to accomplish the task. First, visual features
with bag-of-words representation, not single vector, are extracted to
characterize the image. To improve the performance, the idea of multi-view
learning is implemented and three kinds of features are provided, each one
corresponds to a single view. The information from three views is complementary
to each other, which can be unified together. Then a new distance function is
designed for bags by computing the weighted sum of the distances between
instances. The technique of metric learning is explored to construct a
data-dependent distance metric to measure the relationships between instances,
meanwhile between bags and images, more accurately. Last, a novel approach,
called MVML, is proposed, which optimizes the joint probability that every
image is similar with its nearest image. MVML learns multiple distance metrics,
each one models a single view, to unifies the information from multiple views.
The method can be solved by alternate optimization iteratively. Gradient ascent
and positive semi-definite projection are utilized in the iterations. Distance
comparisons verified that the new bag distance function is prior to previous
functions. In model evaluation, numerical experiments show that MVML with
multiple views performs better than single view condition, which demonstrates
that our model can assemble the complementary information efficiently and
measure the distance between images more precisely. Experiments on influence of
parameters and instance number validate the consistency of the method.
