  We present a method for visual object classification using only a single
feature, transformed color SIFT with a variant of Spatial Pyramid Matching
(SPM) that we called Sliding Spatial Pyramid Matching (SSPM), trained with an
ensemble of linear regression (provided by LINEAR) to obtained state of the art
result on Caltech-101 of 83.46%. SSPM is a special version of SPM where instead
of dividing an image into K number of regions, a subwindow of fixed size is
slide around the image with a fixed step size. For each subwindow, a histogram
of visual words is generated. To obtained the visual vocabulary, instead of
performing K-means clustering, we randomly pick N exemplars from the training
set and encode them with a soft non-linear mapping method. We then trained 15
models, each with a different visual word size with linear regression. All 15
models are then averaged together to form a single strong model.
