  A statistical attention localization (SAL) method is proposed to facilitate
the object classification task in this work. SAL consists of three steps: 1)
preliminary attention window selection via decision statistics, 2) attention
map refinement, and 3) rectangular attention region finalization. SAL computes
soft-decision scores of local squared windows and uses them to identify salient
regions in Step 1. To accommodate object of various sizes and shapes, SAL
refines the preliminary result and obtain an attention map of more flexible
shape in Step 2. Finally, SAL yields a rectangular attention region using the
refined attention map and bounding box regularization in Step 3. As an
application, we adopt E-PixelHop, which is an object classification solution
based on successive subspace learning (SSL), as the baseline. We apply SAL so
as to obtain a cropped-out and resized attention region as an alternative
input. Classification results of the whole image as well as the attention
region are ensembled to achieve the highest classification accuracy.
Experiments on the CIFAR-10 dataset are given to demonstrate the advantage of
the SAL-assisted object classification method.
