When applied to probabilistic categorial grammar learning, the Minimum Description Length principle outperforms Maximum Likelihood Estimation. Smoothing does not bridge the gap between the two approaches.

Statistical shape models are used widely as a basis for segmenting and interpreting images. A major drawback of this approach is the need to identifying corresponding points across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we describe an efficient method that automatically defines a correspondence across the set of shapes...

In the context of part segmentation from 2D edge images, this paper presents some interesting results with a novel method that addresses the problem of ltering a redundant set of part hypotheses that retains only those that are likely to correspond to actual parts. In the proposed method, supporting evidence for hypotheses are put in competition in a Minimum Description-Length (MDL) framework t...

In the context of part segmentation from 2D edge images, this paper presents some interesting results with a novel method that addresses the problem of ltering a redundant set of part hypotheses that retains only those that are likely to correspond to actual parts. In the proposed method, supporting evidence for hypotheses are put in competition in a Minimum Description-Length (MDL) framework t...

Suppose we are given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Leng...

The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coeecients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coeecients of the eigenimages are determined. Instea...

We present a transliteration system that introduces minimum description length training for transliteration and combines it with discriminative modeling. We apply the proposed approach to transliteration from English to 8 non-Latin scripts, with promising results.

Clustering is one of the major data mining tasks. So far, the database and data mining literature lacks systematic study of cluster descriptions, which are essential to provide the user with understandable knowledge of the clusters and support further interactive exploration. In this paper, we introduce novel description formats leading to more descriptive power. We define two alternative probl...

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline methods for addressing concept shifts and to apply them...

The automatic reconnection of linear segments is a problem often encountered in image analysis. This article proposes a procedure for performing this task. Tuning parameters of the proposed procedure can either be chosen manually, or chosen automatically by a method developed in this note. This automatic method is based on the minimum description length principle. The procedure is applied to so...