We introduce quaternionic Gabor filters for the classification of local image structure. These filters are constructed as windowed basis functions of the quaternionic Fourier transform. We show that – in contrast to the 2D complex Gabor filters – the quaternionic Gabor filters are intrinsically 2D filters. A generalized phase concept is introduced and compared to the classical one. It is shown ...

The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive Classification (TC) problems. Specifically, we formulate a Local Learning Regularizer (LL-Reg) which leads to a solution with the property that the label of each data point can be well predicted based on its...

This paper is about using multiple types of information for classification of networked data in the transductive setting: given a network with some nodes labeled, predict the labels of the remaining nodes. One method recently developed for doing such inference is a guilt-by-association model. This method has been independently developed in two different settings. One setting assumes that the ne...

The k-local hyperplane distance nearest neighbors classification (HKNN) builds a non-linear decision surface with maximal local margin in the input space, with invariance inferred from the local neighborhood rather than the prior knowledge, so that it performs very well in many applications. However, it still cannot be comparable with human being in classification on the noisy, the sparse, and ...

In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely S...

Algorithms for classification are proposed based on criteria (information entropy and its production). The feasibility of replacing a given anaesthetic by similar ones in the composition of a complex drug is studied. Some local anaesthetics currently in use are classified using characteristic chemical properties of different portions of their molecules. Many classification algorithms are based ...

In this paper, an efficient local operator, namely the Local Quantization Code (LQC), is proposed for texture classification. The conventional local binary pattern can be regarded as a special local quantization method with two levels, 0 and 1. Some variants of the LBP demonstrate that increasing the local quantization level can enhance the local discriminative capability. Hence, we present a s...

In this chapter we examine several concepts related to local feature descriptor design— namely local patterns, shapes, spectra, distance functions, classification, matching, and object recognition. The main focus is local feature metrics, as shown in Figure 4-1. This discussion follows the general vision taxonomy that will be presented in Chapter 5, and includes key fundamentals for understandi...

Within-network classification, where the goal is to classify the nodes of a partly labeled network, is a semi-supervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked...

It is commonplace knowledge that more and more data is collected everywhere and that the size of data sets available for knowledge discovery is increasing exponentially. On the one hand this is good, because learning with high-dimensional data and complex dependencies may need a large number of examples to give accurate results. On the other hand, there are several learning problems which canno...