Results 1 - 7 of 7

Using Discriminant Eigenfeatures for Image Retrieval

by Daniel L. Swets, John Weng , 1996
"... This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class retrieval ..."
Abstract - Cited by 508 (15 self) - Add to MetaCart
This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class

Short Paper's Using Discriminant Eigenfeatures for Image Retrieval

by Daniel L. Swets, John (juyang Weng
"... Abstract-This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminafing Features for view-based class retrieva ..."
Abstract - Add to MetaCart
retrieval from a large database of widely varying real-world objects presented as "well-framed " views, and compare it with that of the principal component analysis. Index Terms-Principal component analysis, discriminant analysis, eigenfeature, image retrieval, feature selection, face recognition

A General Methodology for Simultaneous Representation and Discrimination of Multiple Object Classes

by Ashit Talukder, David Casasent - Optical Engineering, Special Issue on Advanced Recognition Techniques , 1998
"... In this paper we address a new general method for linear and nonlinear feature extraction for simultaneous representation and classi#cation. We call this approach the maximum representation and discrimination feature #MRDF# method. We develop a novel nonlinear eigenfeature extraction #NLEF# techniqu ..."
Abstract - Cited by 17 (6 self) - Add to MetaCart
In this paper we address a new general method for linear and nonlinear feature extraction for simultaneous representation and classi#cation. We call this approach the maximum representation and discrimination feature #MRDF# method. We develop a novel nonlinear eigenfeature extraction #NLEF

Genetic Feature Subset Selection for Gender Classification: A Comparison Study

by Zehang Sun, George Bebis, Xiaojing Yuan, Sushil J. Louis - In: IEEE Workshop on Applications of Computer Vision , 2002
"... We consider the problem of gender classification from frontal facial images using genetic feature subset selection. We argue that feature selection is an important issue in gender classification and demonstrate that Genetic Algorithms (GA) can select good subsets of features (i.e., features that enc ..."
Abstract - Cited by 39 (11 self) - Add to MetaCart
that encode mostly gender information), reducing the classification error. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space. Genetic Algorithms (GAs) are then employed to select a subset of features from the low

Model-based Characterization of Mammographic Masses

by Sven-rené Von Der Heidt, Matthias Elter, Thomas Wittenberg, Dietrich Paulus, Universität Koblenz-landau
"... Abstract. The discrimination of benign and malignant types of mammographic masses is a major challenge for radiologists. The classic eigenfaces method was recently adapted for the detection of masses in mammograms. In the work at hand we investigate if this method is also suited for the problem of d ..."
Abstract - Add to MetaCart
Abstract. The discrimination of benign and malignant types of mammographic masses is a major challenge for radiologists. The classic eigenfaces method was recently adapted for the detection of masses in mammograms. In the work at hand we investigate if this method is also suited for the problem

Corresponding author:

by Boris Danev, Toshio Kamei, Toshio Kamei, Boris Danev, Toshio Kamei
"... Answers to questions: 1. What is the original contribution of this work? The novelty of our work consists in introducing a two-step PCA for extract-ing spectral eigenfeatures in ngerprints that can be eectively matched by Dynamic Programming (DP). 2. Why should this contribution be considered import ..."
Abstract - Add to MetaCart
Answers to questions: 1. What is the original contribution of this work? The novelty of our work consists in introducing a two-step PCA for extract-ing spectral eigenfeatures in ngerprints that can be eectively matched by Dynamic Programming (DP). 2. Why should this contribution be considered

Quantized embeddings: An efficient and universal nearest neighbor method for cloud-based image retrieval

by Shantanu Rane, Petros Boufounos, Anthony Vetro , 2013
"... We propose a rate-efficient, feature-agnostic approach for encoding image features for cloudbased nearest neighbor search. We extract quantized random projections of the image features under consideration, transmit these to the cloud server, and perform matching in the space of the quantized project ..."
Abstract - Cited by 4 (4 self) - Add to MetaCart
projections. The advantage of this approach is that, once the underlying feature extraction algorithm is chosen for maximum discriminability and retrieval performance (e.g., SIFT, or eigen-features), the random projections guarantee a rate-efficient representation and fast server-based matching
Results 1 - 7 of 7