On Relation of Image Compression and Image Association

Article type: Research Article

Authors: Skarbek, Władysław

Affiliations: Polish-Japanese Institute of Computer Techniques, Koszykowa 86, 02-008 Warszawa, Poland. [email protected]

Abstract: Based on two image compression schemes (MIT and RNS), it is shown that it is possible to associate similar object images using their intermediate representation. Thus both methods can be applied to large image database for both goals: high quality image compression and reliable search for queries by image content. MIT scheme of Moghaddam and Pentland is specialized to face images. It moves image comparison task from high dimensional image space to low dimensional principal subspace spanned on eigenfaces. The closest point in the subspace is used for image association. RNS scheme of the author represents images (not limited to a certain scene type) by recurrent neural subnetworks which together with a competition layer create an associative memory. The single recurrent subnetwork Ni is designed for the i-th image and it implements a stochastic nonlinear operator Fi. It can be shown that under realistic assumptions Fi has a unique attractor which is located in the vicinity of the original image. When at the input a noisy, incomplete or distorted image is presented, the associative recall is implemented in two stages. Firstly, a competition layer finds the most invariant subnetwork. Next, the selected recurrent subnetwork reconstructs in few iterations the original image.

DOI: 10.3233/FI-1997-323410

Journal: Fundamenta Informaticae, vol. 32, no. 3-4, pp. 359-371, 1997