BIP: A dimensionality reduction for image indexing

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...

On Dimensionality Reduction for Indexing and Retrieval of Large-Scale Solar Image Data

This work investigates the applicability of several dimensionality-reduction techniques for large-scale solar data analysis. Using a solar benchmark dataset that contains images of multiple types of phenomena, we investigate linear and non-linear dimensionality reduction methods in order to reduce our storage and processing costs and maintain a good representation of our data in a new vector sp...

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 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