The problem of using pictures of objects captured under ideal
imaging conditions (here referred to as in vitro) to recognize objects in
natural environments (in situ) is an emerging area of interest in computer
vision and pattern recognition. Examples of tasks in this vein include
assistive vision systems for the blind and object recognition for mobile
robots; the proliferation of image databases on the web is bound to lead to
more examples in the near future. Despite its importance, there is still a need
for a freely available database to facilitate study of this kind of
training/testing dichotomy. In this work one of our contributions is a new
multimedia database of 120 grocery products, GroZi-120. For every product, two
different recordings are available: in vitro images extracted from online
grocery websites, and in situ images extracted from camcorder video collected
inside a grocery store. As an additional contribution, we present the results
of applying three commonly used object recognition/ detection algorithms (color
histogram matching, SIFT matching, and boosted Haar-like features) to the
dataset. Finally, we analyze the successes and failures of these algorithms
against product type and imaging conditions, both in terms of recognition rate
and localization accuracy, in order to suggest ways forward for further
research in this domain.
Pre-2018 CSE ID: CS2007-0877