Results 211 to 220 of about 533,095 (268)
Some of the next articles are maybe not open access.
Feature Selection and Feature Extraction: Highlights
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2021In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction.
Hiu-Man Wong +7 more
openaire +1 more source
2016
Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti +3 more
openaire +2 more sources
Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti +3 more
openaire +2 more sources
Feature extraction in the Neocognitron
IEEE International Conference on Neural Networks, 1988The authors present theoretical and numerical developments in the understanding of feature extraction in the Neocognitron. First, they show that the feature extraction process is equivalent to a generalized nonlinear discriminant. Second, they show that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of
Ken Johnson, Cindy Daniell, Jerry Burman
openaire +2 more sources
Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He, Li Wang 0002, Jean Guibert
openaire +1 more source
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He, Li Wang 0002, Jean Guibert
openaire +1 more source
2012
In the previous chapters we have examined static and dynamic methods of program analysis. These features must be translated into mathematical representations and birthmarks to be useful. Furthermore, mathematical representations may be embedded in other mathematical types to make birthmarks more amenable to similarity comparisons and for use in ...
Cesare, S, Xiang, Yang
openaire +1 more source
In the previous chapters we have examined static and dynamic methods of program analysis. These features must be translated into mathematical representations and birthmarks to be useful. Furthermore, mathematical representations may be embedded in other mathematical types to make birthmarks more amenable to similarity comparisons and for use in ...
Cesare, S, Xiang, Yang
openaire +1 more source
Redundancy in Feature Extraction
IEEE Transactions on Computers, 1971Given two random variables X and Y, a definition is offered that gives a condition for Y to be redundant with respect to X. It is shown that if such redundancy exists, then observations on Y, i.e., pattern vector elements related to Y, can be eliminated without increasing the classification error.
openaire +3 more sources
A feature fusion method for feature extraction
SPIE Proceedings, 2012The automatic target recognition based on image fusion refers to the fusion process using the target images provided by a variety of sensors, so as to improve the recognition accuracy and robustness and to obtain better recognition performance.
Dejun Tang +3 more
openaire +2 more sources
Pattern Recognition, 1971
Abstract This paper describes methods for extracting pattern-synthesizing features. A set of patterns is expressed as a Boolean matrix, allowing the problem of feature extraction to be viewed as one of factoring this matrix. Feature extraction methods based on matrix factorization and pattern intersection are presented. Attribute inclusion is defined
openaire +2 more sources
Abstract This paper describes methods for extracting pattern-synthesizing features. A set of patterns is expressed as a Boolean matrix, allowing the problem of feature extraction to be viewed as one of factoring this matrix. Feature extraction methods based on matrix factorization and pattern intersection are presented. Attribute inclusion is defined
openaire +2 more sources
Pronunciation Feature Extraction
2005Automatic pronunciation scoring makes novel applications for computer assisted language learning possible. In this paper we concentrate on the feature extraction. A relatively large feature vector with 28 sentence- and 33 word-level features has been designed.
Christian Hacker +5 more
openaire +1 more source
Feature Extraction Through LOCOCODE
Neural Computation, 1999Low-complexity coding and decoding (LOCOCODE) is a novel approach to sensory coding and unsupervised learning. Unlike previous methods, it explicitly takes into account the information-theoretic complexity of the code generator. It computes lococodes that convey information about the input data and can be computed and decoded by low-complexity ...
Hochreiter, Sepp, Schmidhuber, Jürgen
openaire +3 more sources