The principled non-rigid registration of groups of images requires a fully groupwise objective function. We consider the problem as one of finding the optimal dense correspondence between the images in the set, where optimality is defined using the Minimum Description Length (MDL) principle, that the transmission of a model of the data, together with the parameters of that model, should be as s...

Discovering conceptually interesting and repetitive substructures in a structural data improves the ability to interpret and compress the data. The substructures are evaluated by their ability to describe and compress the original data set using the domain’s background knowledge and the minimum description length (MDL) of the data. Once discovered, the substructure concept is used to simplify t...

A minimum description length (MDL) and stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and implementation of this approach to practical problems are the focuses of the study. Particularly, we provide both algorithms and a package of S language programs for computing the stochastic complexity and proceeding with the a...

An approach inspired by the Minimum Description Length (MDL) principle is proposed for adaptively selecting features during online learning based on their usefulness in improving the objective. The approach eliminates noisy or useless features from the optimization process, leading to improved loss. Several algorithmic variations on the approach are presented. They are based on using a Bayesian...

Finding point correspondences plays an important role in automatically building statistical shape models from a training set of 3D surfaces. For the point correspondence problem, Davies et al. [1] proposed a minimum-description-length-based objective function to balance the training errors and generalization ability. A recent evaluation study [2] that compares several well-known 3D point corres...

A translation-invariant denoising method based on the minimum description length (MDL) criterion and treestructured best-basis algorithms is presented. A collection of signal models is generated using an extended library of orthonormal wavelet-packet bases, and an additive cost function, approximately representing the MDL principle, is derived. We show that the minimum description length of the...

This paper addresses the problem of tracking an arbitrary region in a sequence of images, given a pre-computed velocity field. Such a problem is of importance in applications ranging from video surveillance to video database search. The algorithm presented here formulates tracking as an estimation problem. We propose, as our estimation criterion, a precise description length measure that quanti...

s 859 3 933 1.2 – 168 19 DBLP 6 980 19 13.0 19 15 4 Dialect 1 334 506 16.1 389 56 37 DNA Amp. 4 590 392 1.5 365 39 54 Mammals 2 183 124 20.5 122 50 13 Mushroom 8 192 112 19.3 112 175 59 Newsgroups 400 800 3.5 398 17 17 NSF Abstracts 12 841 4 894 0.9 – 1835 2098 Paleo 501 139 5.1 46 96 19 Newsgroups is a subset of the well-known 20Newsgroups dataset,7 containing, for 400 posts from 4 newsgroups8...

We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to ...

This paper derives several model selection criteria for generalized linear models (GLMs) following the principle of Minimum Description Length (MDL). We focus our attention on the mixture form of MDL. Normal or normal-inverse gamma distributions are used to construct the mixtures, depending on whether or not we choose to account for possible over-dispersion in the data. For the latter, we use E...