Authors:
Mohammad Naeem
and
Pascal Matsakis
Affiliation:
University of Guelph, Canada
Keyword(s):
Image Descriptors, Relative Position Descriptors, Spatial Relationships, Affine Invariance.
Related
Ontology
Subjects/Areas/Topics:
Feature Selection and Extraction
;
Information Retrieval and Learning
;
Knowledge Acquisition and Representation
;
Pattern Recognition
;
Theory and Methods
Abstract:
A relative position descriptor is a quantitative representation of the relative position of two spatial objects. It
is a low-level image descriptor, like colour, texture, and shape descriptors. A good amount of work has been
carried out on relative position description. Application areas include content-based image retrieval, remote
sensing, medical imaging, robot navigation, and geographic information systems. This paper reviews the
existing work. It identifies the approaches that have been used as well as the properties that can be expected
from relative position descriptors. It compares and provides a brief overview of various descriptors, including
their main properties, strengths and limitations, and it suggests areas for future work.