Classification using distance nearest neighbours

This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space, than those which are further away. Our approach builds on previous work by Holmes and Adams (2002, 2003) and Cucala et al. (2008). Our work shares many of the advantages of these approaches in providing a probabilistic basis for the statistical inference. In comparison to previous work, we present a more efficient computational algorithm to overcome the intractability of the Markov random field model. The results of our algorithm are encouraging in comparison to the k-nearest neighbour algorithm.Comment: 12 pages, 2 figures. To appear in Statistics and Computin

Combining Multiple Classifiers with Dynamic Weighted Voting

When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme

A Hierarchical Approach to Multimodal Classification

Abstract. Data models that are induced in classifier construction often consists of multiple parts, each of which explains part of the data. Classi-fication methods for such models are called the multimodal classification methods. The model parts may overlap or have insufficient coverage. How to deal best with the problems of overlapping and insufficient cov-erage? In this paper we propose hierarchical or layered approach to this problem. Rather than seeking a single model, we consider a series of models under gradually relaxing conditions, which form a hierarchical structure. To demonstrate the effectiveness of this approach we imple-mented it in two classifiers that construct multi-part models: one based on the so-called lattice machine and the other one based on rough set rule induction. This leads to hierarchical versions of the classifiers. The classification performance of these two hierarchical classifiers is compared with C4.5, Support Vector Machine (SVM), rule based classifiers (with the optimisation of rule shortening) implemented in Rough Set Explo-ration System (RSES), and a method combining k-nn with rough set rule induction (RIONA in RSES). The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers

Improved Scheme for Object Searching Using Moment Invariants

Third IEEE Pacific Rim Conference on Multimedia, Hsinchu, Taiwan, December 16–18, 2002For multimedia retrieval application, shape is always a conspicuous element of an object. Moment-based approaches are widely used for shape description due to its translation, scaling and rotation invariance. Moment invariants are defined in the continuous domain. However, when considering the digital images in practice, quantization errors are introduced. Thus, the moment invariants calculated might not be truly invariant. This paper presents an analysis of quantization effects on four moment-based approaches of both regular and irregular objects. From the analysis, the scaling errors for all approaches are large when the scaling factor is less than 0.5. Moreover, the rotational errors are big for the objects rotated other than the multiples of 90°. Our experimental results show that Dudani moment invariants suffer the largest error for overall sensitivity, while Affine moment invariants show the smallest. Furthermore, this error analysis has also been applied successfully to object searching applications using a threshold selection scheme.Department of Electronic and Information EngineeringRefereed conference pape

Shape Description for Automatically Structuring Graphical Data

This work explores automatic object recognition and semantic capture in vector graphics through shape description. The low-level graphical content of graphical documents, such as a map or architectural drawing, are often captured manually and the encoding of the semantic content seen as an extension of this. The large quantity of new and archived graphical data available on paper makes automatic structuring of such graphical data desirable. Contour shape description techniques, such as Fourier descriptors, moment invariants play an important role in systems for object recognition and representation. However, most work carried out in this area has concentrated on categories of object boundaries representing very specific shapes (for example, a particular type of aircraft). Two classifiers were implemented and proved accurate in their automatic recognition of objects from drawings in different domains. Classical classifier combination techniques were used to improve performance. Further work will employ more complex fusion techniques and it is envisaged they will be used in combination with recognition based on object context using various modelling methods. A demonstration system has been constructed using all these techniques