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Abstract:
A key ingredient in the design of visual object classification
systems is the identification of relevant class specific
aspects while being robust to intra-class variations. While
this is a necessity in order to generalize beyond a given set
of training images, it is also a very difficult problem due to
the high variability of visual appearance within each class.
In the last years substantial performance gains on challenging
benchmark datasets have been reported in the literature.
This progress can be attributed to two developments: the
design of highly discriminative and robust image features
and the combination of multiple complementary features
based on different aspects such as shape, color or texture.
In this paper we study several models that aim at learning
the correct weighting of different features from training
data. These include multiple kernel learning as well as
simple baseline methods. Furthermore we derive ensemble
methods inspired by Boosting which are easily extendable to
several multiclass setting. All methods are thoroughly evaluated
on object classification datasets using a multitude of
feature descriptors. The key results are that even very simple
baseline methods, that are orders of magnitude faster
than learning techniques are highly competitive with multiple
kernel learning. Furthermore the Boosting type methods
are found to produce consistently better results in all experiments.
We provide insight of when combination methods
can be expected to work and how the benefit of complementary
features can be exploited most efficiently.
