Manually creating an object category dataset
requires a lot of hard work and wastes a large amount of time.
Having an automatic means for collecting images that represent
different objects is crucial for the scalable and practical
expansion of these datasets. In this work, a methodology to
automatically re-rank the images returned from a web search
engine is proposed to improve the precision of the retrieved
results. The proposed system works in an incremental way to
improve the learnt object model and achieve better precision in
each iteration. Images along with their meta data are ranked,
then re-filtered based on their textual and visual features to
produce a robust set of seed images. These images are used
in learning weighted distances between the images which are
used to incrementally expand the collected dataset. Using our
method, we automatically gather very large object category
datasets. We also improve the image ranking performance of
the retrieved results over web search engines and other batch
methods.