Dimensionality Reduction by Canonical Contextual Correlation Projections

A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical Fisher linear discriminant analysis (LDA) and useful, for example, in supervised segmentation tasks in which high-dimensional feature vector describes the local structure of the image. In general, the main idea of the technique is applicable in discriminative and statistical modelling that involves contextual data. LDA is a basic, well-known and useful technique in many applications. Our contribution is that we extend the use of LDA to cases where there is dependency between the output variables, i.e., the class labels, and not only between the input variables. The latter can be dealt with in standard LDA. The principal idea is that where standard LDA merely takes into account a single class label for every feature vector, the new technique incorporates class labels of its neighborhood in its analysis as we...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Marco Loog, Bram van Ginneken, Robert P. W. Duin
• Keywords: Canonical Contextual Correlation, Computer Vision, Contextual Correlation Projection, Contextual Lda Approach, ECCV 2004, Spatial Lda, Standard Lda