Today I read a paper titled “Profile Based Sub-Image Search in Image Databases”
The abstract is:
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.