This paper presents a framework designed for the multi-object detection purposes and adjusted for the application
of product search on the market shelves. The framework uses a single feedback loop and a pattern resizing
mechanism to demonstrate the top effectiveness of the state-of-the-art local features. A high detection rate with
a low false detection chance can be achieved with use of only one pattern per object and no manual parameters
adjustments. The method incorporates well known local features and a basic matching process to create a reliable
voting space. Further steps comprise of metric transformations, graphical vote space representation, two-phase
vote aggregation process and a cascade of verifying filters
