Title
Shape categorization using string kernels
Abstract
In this paper, a novel algorithm for shape categorization is proposed. This method is based on the detection of perceptual landmarks, which are scale invariant. These landmarks and the parts between them are transformed into a symbolic representation. Shapes are mapped into symbol sequences and a database of shapes is mapped into a set of symbol sequences and therefore it is possible to use support vector machines for categorization. The method here proposed has been evaluated on silhouettes database and achieved the highest recognition result reported with a score of 97.85% for the MPEG-7 shape database.
Year
DOI
Venue
2006
10.1007/11815921_32
SSPR/SPR
Keywords
DocType
Volume
symbolic representation,support vector machine,silhouettes database,symbol sequence,mpeg-7 shape database,highest recognition result,novel algorithm,shape categorization,string kernel,scale invariant,perceptual landmark,scale invariance
Conference
4109
ISSN
ISBN
Citations 
0302-9743
3-540-37236-9
6
PageRank 
References 
Authors
0.46
16
4
Name
Order
Citations
PageRank
Mohammad Reza Daliri136618.70
Elisabetta Delponte2504.72
Alessandro Verri31754190.73
V Torre4653186.85