Authors:
Vítor Baccetti Garcia
and
Ricardo da S. Torres
Affiliation:
University of Campinas (Unicamp), Brazil
Keyword(s):
Content-based image retrieval, Shape description, Image foresting transform, Multiscale fractal dimension.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Fractal and Chaos Theory in Image Analysis
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper proposes a new scale-invariant shape descriptor based on the Multiscale Fractal Dimension (MFD). The MFD is a curve that describes boundary complexity and self-affinity characteristics by obtaining fractal dimension values as function of Euclidean morphologic dilation radii. Using this concept, which guarantees rotation and translation invariance, we introduce a new scale-invariant descriptor that is obtained by selecting a relevant fragment of this curve using a sliding window. The novel shape descriptor is compared with the Multiscale Fractal Dimension and four other shape descriptors. Experimental results demonstrate that the new descriptor is scale-invariant and yields very good results in terms of effectiveness performace when compared with well-known shape descriptors.