Result: Robust matching method for scale and rotation invariant local descriptors and its application to image indexing
Title:
Robust matching method for scale and rotation invariant local descriptors and its application to image indexing
Authors:
Source:
Information retrieval technology (Second Asia information retrieval symposium, AIRS 2005, Jeju Island, Korea, October 13-15, 2005, proceedings)Lecture notes in computer science. :601-615
Publisher Information:
New York, NY: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 16 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Documentation, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Indexation, Indexing, Indización, Invariant, Invariante, Loi normale, Gaussian distribution, Curva Gauss, Processus Gauss, Gaussian process, Proceso Gauss, Recherche information, Information retrieval, Búsqueda información
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Systems Information Science, Future University-Hakodate, 116-2 Kamedanakano-cho, Hakodate-shi, Hokkaido, 041-8655, Japan
ISSN:
0302-9743
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17325895
Database:
PASCAL Archive
Further Information
Interest point matching is widely used for image indexing. In this paper we introduce a new distance measure between two local descriptors instead of conventional Mahalanobis distance to improve matching accuracy. From experiments with synthetic images we show that the error distribution of local jet is gaussian but the distribution of the descriptors derived from local jet is not gaussian. Based on the observation, we design a new distance measure between two local descriptors and improve accuracy of point matching. We also reduce the number of candidate points and reduce the computational cost by taking into account the characteristic scale ratio. Experimental results confirm the validity of our method.