Significance of Dimensionality Reduction in Image Processing

Dimensionality reduction by local processing

In this paper we describe a novel approach towards dimensionality reduction of patterns to be classi ed. It consists of local processing of the patterns as an alternative to the well-known global principal component analysis (PCA) algorithm. We use a feed-forward neural network architecture with spatial or spatio-temporal receptive eld connections between the rst two layers that yields a transf...

Image Reduction Using Assorted Dimensionality Reduction Techniques

Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-T...

Dimensionality Reduction for Image Retrieval

Dimensionality reduction methods are of interest in applications such as content based image and video retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve the nearest neighbors of a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dim...

Dimensionality Reduction with Image Data

A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.

Image Restoration, Modelling, and Reduction of Dimensionality