Importance Order Ranking for Texture Extraction: A More Efficient Pooling Operator Than Max Pooling?

Abstract

: Much of convolutional neural network (CNN)’s success lies in translation invariance. The other part resides in the fact that thanks to a judicious choice of architecture, the network is able to make decisions taking into account the whole image. This work provides an alternative way to extend the pooling function, we named rank-order pooling, capable of extracting texture descriptors from images. The rank-order pooling layers are non parametric, independent of the geometric arrangement or sizes of the image regions, and can therefore better tolerate rotations. Rank-order pooling functions produce images capable of emphasizing low/high frequencies, contours, etc. We shows rank-order pooling leads to CNN models which can optimally exploit information from their receptive field.