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2011-08-03 08:46:46|  分类: 默认分类 |  标签: |举报 |字号 订阅

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The Science of Pattern Recognition

Achievements and Perspectives


Robert P.W. Duin1 and El˙zbieta Pekalska2

1 ICT group, Faculty of Electr. Eng., Mathematics and Computer Science

Delft University of Technology, The Netherlands


School of Computer Science, University of ManchesterUnited Kingdom



Summary. Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general.



Like in any science understanding can be built from different, sometimes even opposite viewpoints. We will therefore introduce the main approaches to the science of pattern recognition as two dichotomies of complementary scenarios. They give rise to four different schools, roughly defined under the terms of expert systems, neural networks, structural pattern recognition and statistical pattern recognition.



We will briefly describe what has been achieved by these schools, what is common and what is specific, which limitations are encountered and which perspectives arise for the future. Finally, we will focus on the challenges facing pattern recognition in the decennia to come. They mainly deal with weaker assumptions of the models to make the corresponding procedures for learning and recognition wider applicable. In addition, new formalisms need to be developed.



1 Introduction

1 介绍


We are very familiar with the human ability of pattern recognition. Since our early years we have been able to recognize voices, faces, animals, fruits or inanimate objects. Before the speaking faculty is developed, an object like a ball is recognized, even if it barely resembles the balls seen before. So, except for the memory, the skills of abstraction and generalization are essential to find our way in the world. In later years we are able to deal with much more complex patterns that may not directly be based on sensorial observations.



For example, we can observe the underlying theme in a discussion or subtle patterns in human relations. The latter may become apparent, e.g. only by listening to somebody’s complaints about his personal problems at work that again occur in a completely new job. Without a direct participation in the

events, we are able to see both analogy and similarity in examples as complex as social interaction between people. Here, we learn to distinguish the pattern from just two examples.



The pattern recognition ability may also be found in other biological systems:the cat knows the way home, the dog recognizes his boss from the footsteps or the bee finds the delicious flower. In these examples a direct connection can be made to sensory experiences. Memory alone is insufficient; an important role is that of generalization from observations which are similar,although not identical to the previous ones. A scientific challenge is to find out how this may work.



Scientific questions may be approached by building models and, more explicitly, by creating simulators, i.e. artificial systems that roughly exhibit the same phenomenon as the object under study. Understanding will be gained while constructing such a system and evaluating it with respect to the real object. Such systems may be used to replace the original ones and may even improve some of their properties. On the other hand, they may also perform worse in other aspects. For instance, planes fly faster than birds but are far from being autonomous. We should realize, however, that what is studied in this case may not be the bird itself, but more importantly, the ability to fly.



Much can be learned about flying in an attempt to imitate the bird, but also when differentiating from its exact behavior or appearance. By constructing fixed wings instead of freely movable ones, the insight in how to fly grows.



Finally, there are engineering aspects that may gradually deviate from the original scientific question. These are concerned with how to fly for a long time, with heavy loads, or by making less noise, and slowly shift the point of attention to other domains of knowledge.



The above shows that a distinction can be made between the scientific study of pattern recognition as the ability to abstract and generalize from observations and the applied technical area of the design of artificial pattern recognition devices without neglecting the fact that they may highly profit from each other. Note that patterns can be distinguished on many levels,starting from simple characteristics of structural elements like strokes, through features of an individual towards a set of qualities in a group of individuals,to a composite of traits of concepts and their possible generalizations. A pattern may also denote a single individual as a representative for its population, model or concept. Pattern recognition deals, therefore, with patterns, regularities,characteristics or qualities that can be discussed on a low level of sensory measurements (such as pixels in an image) as well as on a high level of the derived and meaningful concepts (such as faces in images). In this work, we will focus on the scientific aspects, i.e. what we know about the way pattern recognition works and, especially, what can be learned from our attempts to build artificial recognition devices.



A number of authors have already discussed the science of pattern recognition based on their simulation and modeling attempts. One of the first, in the beginning of the sixties, was Sayre [64], who presented a philosophical study on perception, pattern recognition and classification. He made clear that classification is a task that can be fulfilled with some success, but recognition either happens or not. We can stimulate the recognition by focussing on some aspects of the question. Although we cannot set out to fully recognize an individual, we can at least start to classify objects on demand. The way Sayre distinguishes between recognition and classification is related to the two subfields discussed in traditional texts on pattern recognition, namely unsupervised and supervised learning. They fulfill two complementary tasks. They act as automatic tools in the hand of a scientist who sets out to find the regularities in nature.



Unsupervised learning (also related to exploratory analysis or cluster analysis) gives the scientist an automatic system to indicate the presence of yet unspecified patterns (regularities) in the observations. They have to be confirmed (verified) by him. Here, in the terms of Sayre, a pattern is recognized.



Supervised learning is an automatic system that verifies (confirms)the patterns described by the scientist based on a representation defined by him. This is done by an automatic classification followed by an evaluation.



In spite of Sayre’s discussion, the concepts of pattern recognition and classification are still frequently mixed up. In our discussion, classification is a significant component of the pattern recognition system, but unsupervised learning may also play a role there. Typically, such a system is first presented with a set of known objects, the training set, in some convenient representation. Learning relies on finding the data descriptions such that the system can correctly characterize, identify or classify novel examples. After appropriate preprocessing and adaptations, various mechanisms are employed to train the entire system well. Numerous models and techniques are used and their performances are evaluated and compared by suitable criteria. If the final goal is prediction, the findings are validated by applying the best model to unseen data. If the final goal is characterization, the findings may be validated by complexity of organization (relations between objects) as well as by interpretability of the results.




Fig. 1 shows the three main stages of pattern recognition systems: Representation, Generalization and Evaluation, and an intermediate stage of Adaptation[20]. The system is trained and evaluated by a set of examples, the Design Set. The components are:

1显示了模式识别系统的三个主要阶段:表示、推广和评估,还有一个中间过程是适配。这个系统通过一个设计样本集(Design Set)来训练和评估。每个组成部分分别描述如下:


Design Set. It is used both for training and validating the system. Given the background knowledge, this set has to be chosen such that it is representative for the set of objects to be recognized by the trained system.There are various approaches how to split it into suitable subsets for training,validation and testing. See e.g. [22, 32, 62, 77] for details.



Representation. Real world objects have to be represented in a formal way in order to be analyzed and compared by mechanical means such as a computer. Moreover, the observations derived from the sensors or other formal representations have to be integrated with the existing, explicitly formulated knowledge either on the objects themselves or on the class they may belong to. The issue of representation is an essential aspect of pattern recognition and is different from classification. It largely influences the success of the stages to come.



Adaptation. It is an intermediate stage between Representation and Generalization,in which representations, learning methodology or problem statement are adapted or extended in order to enhance the final recognition.This step may be neglected as being transparent, but its role is essential.It may reduce or simplify the representation, or it may enrich it by emphasizing particular aspects, e.g. by a nonlinear transformation of features that simplifies the next stage. Background knowledge may appropriately be (re)formulated and incorporated into a representation. If needed, additional representations may be considered to reflect other aspects of the problem. Exploratory data analysis (unsupervised learning) may be used to guide the choice of suitable learning strategies.



Generalization or Inference. In this stage we learn a concept from a training set, the set of known and appropriately represented examples, in such a way that predictions can be made on some unknown properties of new examples. We either generalize towards a concept or infer a set of general rules that describe the qualities of the training data. The most common property is the class or pattern it belongs to, which is the above mentioned classification task.



Evaluation. In this stage we estimate how our system performs on known training and validation data while training the entire system. If the results are unsatisfactory, then the previous steps have to be reconsidered.



Different disciplines emphasize or just exclusively study different parts of this system. For instance, perception and computer vision deal mainly with the representation aspects [21], while books on artificial neural networks [62],machine learning [4, 53] and pattern classification [15] are usually restricted to generalization. It should be noted that these and other studies with the words “pattern” and “recognition” in the title often almost entirely neglect the issue of representation. We think, however, that the main goal of the field of pattern recognition is to study generalization in relation to representation[20].



In the context of representations, and especially images, generalization has been thoroughly studied by Grenander [36]. What is very specific and worthwhile is that he deals with infinite representations (say, unsampled images),thereby avoiding the frequently returning discussions on dimensionality and directly focussing on a high, abstract level of pattern learning. We like to mention two other scientists that present very general discussions on the pattern recognition system: Watanabe [75] and Goldfarb [31, 32]. They both emphasize the structural approach to pattern recognition that we will discuss later on. Here objects are represented in a form that focusses on their structure.A generalization over such structural representations is very difficult if one aims to learn the concept, i.e. the underlying, often implicit definition of a pattern class that is able to generate possible realizations. Goldfarb argues that traditionally used numeric representations are inadequate and that an entirely new, structural representation is necessary. We judge his research program as very ambitious, as he wants to learn the (generalized) structure of the concept from the structures of the examples. He thereby aims to make explicit what usually stays implicit. We admit that a way like his has to be followed if one ever wishes to reach more in concept learning than the ability to name the right class with a high probability, without having built a proper understanding.



In the next section we will discuss and relate well-known general scientific approaches to the specific field of pattern recognition. In particular, we like to point out how these approaches differ due to fundamental differences in the scientific points of view from which they arise. As a consequence, they are often studied in different traditions based on different paradigms. We will try to clarify the underlying cause for the pattern recognition field. In the following sections we sketch some perspectives for pattern recognition and define a number of specific challenges.


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