This paper presents a novel approach to scribblebased image colorization. In the work reported here we have explored how to exploit the textural information to improve this process. For every scribbled image we extract the most discriminative features using linear discriminant analysis (LDA). After that, the whole image is projected onto a discriminative textural feature space. Our main contrib...

Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. In this way, intern...

We look at a single point in the feature space, two classes, and L classifiers estimating the posterior probability for class !1. Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.

High-dimensional discriminant analysis is of fundamental importance in multivariate statistics. Existing theoretical results sharply characterize different procedures, providing sharp convergence results for the classification risk, as well as the l2 convergence results to the discriminative rule. However, sharp theoretical results for the problem of variable selection have not been established...

This work is an attempt to automatically obtain numerical attributes of physical objects. We propose representing each physical object as a feature vector and representing sizes as linear functions of feature vectors. We train the function in the framework of the combined regression and ranking with many types of fragmentary clues including absolute clues (e.g., A is 30cm long) and relative clu...

In an earlier study, Swain et al. reported on two statistical separability measures which for multiclass feature selection were shown experimentally to be more reliable than divergence. However, the empirical results of that study together with the best theoretical results in the literature left open some practical questions regarding the quantitative characterization of these separability meas...

This working notes paper describes the system proposed by the MIRUtrecht team for static emotion recognition from audio (task Emotion in Music) in the MediaEval evaluation contest 2013. We approach the problem by proposing a scheme comprising data filtering, feature extraction, attribute selection and multivariate regression. The system is based on state-of-the art research in the field and ach...

In this paper, we study two measures of classification complexity based on feature space partitioning: “purity” and “neighborhood separability.” The new measures of complexity are compared with probabilistic distance measures and a number of other nonparametric estimates of classification complexity on a total of 10 databases from the University of Calfornia, Irvine, (UCI)

In this paper we focus our topic on linear separability of two data sets in feature space, including finite and infinite data sets. We first develop a method to construct a mapping that maps original data set into a high dimensional feature space, on which inner product is defined by a dot product kernel. Our method can also be applied to the Gaussian kernels. Via this mapping, structure of fea...

In this paper, the problem of selecting most representative features among a feature set is considered. Two new feature selection algorithms are introduced and their performances are compared with some well-known feature selection algorithms. The algorithms are tested with the iris data set, three artificially generated data sets and a data set obtained from steel surfaces.