  We consider a pipeline for image classification or search based on coding
approaches like Bag of Words or Fisher vectors. In this context, the most
common approach is to extract the image patches regularly in a dense manner on
several scales. This paper proposes and evaluates alternative choices to
extract patches densely. Beyond simple strategies derived from regular interest
region detectors, we propose approaches based on super-pixels, edges, and a
bank of Zernike filters used as detectors. The different approaches are
evaluated on recent image retrieval and fine-grain classification benchmarks.
Our results show that the regular dense detector is outperformed by other
methods in most situations, leading us to improve the state of the art in
comparable setups on standard retrieval and fined-grain benchmarks. As a
byproduct of our study, we show that existing methods for blob and super-pixel
extraction achieve high accuracy if the patches are extracted along the edges
and not around the detected regions.
