Abstract:
Abstract. The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). In this paper we present a method which avoids the computation of the autocorrelation coeffi-cients and which can be applied to a large set of toots commonly used in statis-tical pattern recognition. We will discuss different scenarios of using the auto-correlations and we will show that the order of autocorrelations is no longer an obstacle.
Citations
|
4514
|
Statistical Learning Theory
– Vapnik
- 1998
|
|
2138
|
UCI Repository of Machine Learning Databases
– Merz, Murphy
- 1996
|
|
630
|
An introduction to Support Vector Machines and other Kernel-based learning methods
– Cristianini, Shawe-Taylor
- 2000
|
|
101
|
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback
– Keogh, Pazzani
- 1998
|
|
65
|
algorithms for PCA and SPCA
– EM
- 1998
|
|
51
|
Learning kernel classifiers: Theory and algorithms
– Herbrich
- 2002
|
|
51
|
EM algorithms for PCA and SPCA
– Roweis
- 1998
|
|
32
|
Kernel PCA pattern reconstruction via approximate pre-images
– Schölkopf
- 1998
|
|
22
|
A review of dimension reduction techniques
– Carreira-Perpinan
- 1997
|
|
20
|
Face recognition system using local autocorrelations and multiscale integration
– Goudail, Lange, et al.
- 1996
|
|
16
|
Modeling Spatial Dependencies for Mining Geospatial Data
– Chawla, Shekhar, et al.
- 2001
|
|
16
|
A face recognition method using higher order local autocorrelation and multivariate analysis
– Kurita, Otsu, et al.
- 1992
|
|
12
|
Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image
– Hotta, Kurita, et al.
- 1998
|
|
12
|
Scale and rotation invariant recognition method using higher-order local autocorrelation features of log-polar image
– Kurita, Hotta, et al.
- 1998
|
|
12
|
Nth-order autocorrelations in pattern recognition
– McLaughlin, Raviv
- 1968
|
|
10
|
Scale-invariant image recognition based on higher order autocorrelation features
– Kreutz, Völpel, et al.
- 1996
|
|
7
|
Higher-order statistics in visual object recognition
– Breuel
- 1993
|
|
4
|
Higher order autocorrelations for pattern classification
– Popovici, Thiran
- 2001
|
|
1
|
Higher-Order Statistics in Visual Object Recognition
– Brenel
|
|
1
|
Carreira-Perpifin, "A Review of Dimension Reduction Techniques
– unknown authors
- 1997
|
|
1
|
and Z.Foldvari, "Arline-Invariant Texture Classification Using Regularity Features
– Cherverikov
|
|
1
|
PCA in Autocorrelation Space
– Popevici, Thiran
- 2002
|
|
1
|
Thiran, "Higher Order Autocorrelations for Pattern Classification
– Popovici, P
- 2001
|
|
1
|
PCA in Autocorrelation Space
– Popovici, Thiran
- 2002
|
|
1
|
Higher-Order Statistics
– Breuel
- 1993
|
|
1
|
Carreira-Perpiñán, “A Review of Dimension Reduction Techniques
– Á
- 1997
|
