As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reasons account for this trend: the first is that compared to the data label, pair...

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre-selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual rep...

We present a new method to compute depth-three threshold circuits for pattern classi cation problems. The rst layer of the circuits is calculated from a sample set S of the classi cation problem by a local search strategy that minimises the error on S for each individual gate. The local search is based on simulated annealing with the logarithmic cooling schedule c(k) = = ln (k + 2). The paramet...

This paper discusses the concept of object’s shape identification using local binary pattern technique (LBP). Since LBP is computationally simple it has been utilized successfully for recognition of various objects. LBP which has the potential to be used in various identification related fields was applied on a number of different shaped objects, the process converted the given image in to 3x3...

We study a class of super-rough growth models whose structure factor satisfies the Family-Vicsek scaling. We demonstrate that a macroscopic background spontaneously develops in the local surface profile, which dominates the scaling of the local surface width and the height difference. The shape of the macroscopic background takes a form of a finite-order polynomial whose order is decided from t...

The symmetries in a neighbourhood of a gray value image are modelled by conjugate harmonic function pairs. These are shown to be a suitable curve linear coordinate pair, in which the model represents a neighbourhood. In this representation the image parts, which are symmetric with respect to the chosen function pair, have iso-gray value curves which are simple lines or parallel line patterns. T...

Individual cellular automata rules are attractive models for a range of biological and physical self-assembling systems. While coexpression and coevolution are common in such systems, ensembles of cellular automata rules remain poorly understood. Here we report the first known analysis of the equally weighted ensemble of all elementary cellular automata (ECA) rules. Ensemble dynamics reveal per...

Individual cellular automata rules are attractive models for a range of biological and physical self-assembling systems. While coexpression and coevolution are common in such systems, ensembles of cellular automata rules remain poorly understood. Here we report the first known analysis of the equally weighted ensemble of all elementary cellular automata (ECA) rules. Ensemble dynamics reveal per...

Local discriminative learning methods approximate a target function (a posteriori class probability function) directly by partitioning the feature space into a set of local regions, and appropriately modeling a simple input}output relationship (function) in each one. This paper presents a new method for judiciously partitioning the input feature space in order to accurately represent the target...

Background modeling is an important issue in video surveillance. A sophisticated and adaptive background model can be used to detect moving objects which are segregated from the scene in each image frame of the video via the background subtraction process. Many background subtraction methods are proposed for video acquired by a stationary camera, assuming that the background exhibits stationary...