  Sub-image search with high accuracy in natural images still remains a
challenging problem. This paper proposes a new feature vector called profile
for a keypoint in a bag of visual words model of an image. The profile of a
keypoint captures the spatial geometry of all the other keypoints in an image
with respect to itself, and is very effective in discriminating true matches
from false matches. Sub-image search using profiles is a single-phase process
requiring no geometric validation, yields high precision on natural images, and
works well on small visual codebook. The proposed search technique differs from
traditional methods that first generate a set of candidates disregarding
spatial information and then verify them geometrically. Conventional methods
also use large codebooks. We achieve a precision of 81% on a combined data set
of synthetic and real natural images using a codebook size of 500 for top-10
queries; that is 31% higher than the conventional candidate generation
approach.
