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based techniques has been evaluated showing that the
presented multi-scale approach outperforms state-of-
the-art visual attention models. The experiments con-
firmed that bottom-up recognition is more difficult
but it is also easier to apply to arbitrary objects and
more efficient than specialized detectors that need to
be trained and applied separatly in a sliding window
approach. These properties and the independence of
annotations for most parts is an important step to-
ward automated object recognizer training. It has also
been shown that promising recognition rates can be
obtained for some object categories on the VOC2011
database.
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