Authors:
B. Balasanjeevi
and
C. Chandra Sekhar
Affiliation:
Indian Institute of Technology, India
Keyword(s):
Image Descriptor, Computer Vision, Image Matching.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Geometry and Modeling
;
Image Understanding
;
Image-Based Modeling
;
Object Recognition
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
Abstract:
This paper proposes a method for extracting image descriptors using intensity binning. It is based on the fact
that, when the intensities of the interest regions are quantized, the pixels retain their bin labels under common
image deformations, up to a certain degree of perturbation. Consequently, the spatial configuration and the
shape of the connected regions of pixels belonging to each bin become resilient to noise, which, as a whole,
capture the topography of the intensity map pertaining to that region. We examine the effect of classical
image deformations on this representation and seek to find a compact yet robust representation which remains
unperturbed in the presence of noise and image deformations. We use Oxford dataset in our experiments and
the results show that the proposed descriptor gives a better performance than the existing methods for matching
two images under common image deformations.