Immersive Visualization of SAR Images Using Nonnegative Matrix Factorization

Abstract

Interactive visual data mining, where the user plays a key role in learning process, has gained high attention in data mining and human-machine communication. However, this approach needs Dimensionality Reduction (DR) techniques to visualize image collections. Although the main focus of DR techniques lays on preserving the structure of the data, the occlusion of images and inefficient usage of display space are their two main drawbacks. In this work, we propose to use Non-negative Matrix Factorization (NMF) to reduce the dimensionality of images for immersive visualization. The proposed method aims to preserve the structure of data and at the same time reduce the occlusion between images by defining regularization terms for NMF. Experimental validations performed on two sets of image collections show the efficiency of the proposed method in respect to controlling the trade-off between structure preserving and less occluded visualization.