NDLI: Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method

Content Provider	Springer Nature Link
Author	Fotopoulou, F. Laskaris, N. Ecomou, G. Fotopoulos, S.
Copyright Year	2011
Abstract	A novel method for shape analysis and similarity measurement is introduced based on a time series matching approach. It applies to shapes represented through one-dimensional signals and has as objectives to utilize efficiently the provided information and to optimize the shape matching process. The new technique is tested on boundaries from leaf images, after their conversion into 1D sequences using either the Centroid Contour Distance (CCD) or the Angle code (AC) measurements. In the core of the new method lies the ‘time delay’-based transformation of a given 1D sequence to an ensemble of vectors embedded in a multivariate phase space. The resulting point set is considered as representative of the leaf identity. Inter-leaf comparisons are carried out in a pairwise fashion by employing the multidimensional, Wald–Wolfowitz, statistical test for the ‘two-sample problem’, which implicitly performs shape matching and similarity quantification. The comparative experimentation shows that the complexity of our method is moderate, while the leaf retrieval performance, compared to that achieved by standard matching procedures usually employed with the CCD and AC representations, is greatly improved.
Starting Page	381
Ending Page	392
Page Count	12
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	16
Issue Number	3
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2011-12-04
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Shape descriptors Shape matching Leaf image retrieval Time-delay embedding Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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