模式识别中英文

1、The Science of Pattern RecognitionAchievements and Perspectives Robert P.W. Duin1 and Elzbieta P_ ekalska21 ICT group, Faculty of Electr. Eng., Mathematics and Computer ScienceDelft University of Technology, The N2 School of C

2、omputer Science, University of Manchester, United Kingdompekalskacs.man.ac.uk 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 rec

3、ognizing 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 rec

4、ognition 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 wil

5、l 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 recognit

6、ion.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

7、 weaker assumptions of the models to make the corresponding procedures for learning and recognition wider applicable. In addition, new formalisms need to be developed.IntroductionWe are very familiar with the human ability of pattern recognition. Since our early years we have been able to recognize

8、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. I

9、n 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 somebodys compl

10、aints about his personal problems at work that again occur in a completely new job. Without a direct participation in theevents, 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 example

11、s.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

12、 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 s

13、ystems 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 othe

14、r 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 a

15、n 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. The

16、se 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 general

17、ize 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 lik

18、e 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

19、, 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 a

20、spects, 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

21、 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 recog

22、nition 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

23、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 t

24、he 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 patt

25、erns 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 Sayres discussion, the concepts of pattern recognition and classification are still frequently mixed up. In our discussion, classification is a

26、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

27、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

28、 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 t

29、he three main stages of pattern recognition systems: Representation, Generalization and Evaluation, and an intermediate stage of Adaptation20. The system is trained and evaluated by a set of examples, the Design Set. The components are: Design Set. It is used both for training and validati

30、ng 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 de

31、tails. 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, explici

32、tly 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 interm

33、ediate 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 repres

34、entation, 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

35、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 example

36、s, 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 mentio

37、ned 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 exclusiv

38、ely 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 t

39、hese 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 representation20.In the context of repres

40、entations, 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

41、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

42、 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

43、 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 m

44、ake 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. 模式识别研究的成果与展望 自动模式识别通常被认为是这样的一个工程领域：专注于开发和

45、评价模仿或辅助人类识别模式能力的系统，但是也可能被认为是这样的一门科学：学习人类(或其它生物系统)在所处环境中发现、区别和找出特征从而标识出观察结果的本领。模式识别中工程的观点是试图建立模拟生物识别能力的系统，通过工程中的实践，总的来说，科学上的理解在模式识别中的技术需求方面得到了发展。 象任何科学一样，对模式识别的理解能够从不同方向来建立，有时甚至是相反的观点。我们将介绍模式识别科学中的主要方法，即两种不同方向且各有两个不同种类的技术，这些技术产生了四个不同学派，粗略地可以定义为：专家系统，神经网络，结构模式识别和统计模式识别。 我们将简要地描述这四个学派的发展成果，它们之间的相同点及不同点

46、，它们各自碰到的局限性及未来发展的展望。最后，我们再来看模式识别在未来几十年所面临的挑战,这个挑战主要是解决在学习和识别更大范围适用性时所碰到的为建立相应处理的模型的脆弱问题。再有就是需要发展新的模式识别形式。介绍  对于人类的识别能力我们是非常熟悉的。因为我们在早些年就已经会开发识别声音、脸、动物、水果或简单不动的东西的技术了。在开发出说话技术之前，一个象球的东西，甚至看上去只是象个球，就已经可以被识别出来了。所以除了记忆，抽象和推广能力是推进模式识别技术的关键技术。最近几年我们已可以处理更复杂的模式，这种模式可能不是直接基于通过感知器观察出来的。 例如，我们能够观察发现某个讨论会

47、的中心议题或人与人之间关系的微妙的模式。后面一种模式是可能可以被明显观察到，例如倾听某人在新的工作中因人际关系问题而产生的抱怨，我们不用切身其中就能够发现这种相似和相同的例子，其复杂性莫过于人与人之间的社会相互影响。这里我们要学会区分只是从两个例子中得到的模式。 模式识别的能力也可以在其它生物中被发现到：猫可以知道回家的路，狗能够识别主人的脚印，蜜蜂会发现它要采蜜的花。这些例子中每一个直接联结都是通过感观来实现的。不只是记忆方面，推广能力是重要的一方面，从观察到的相似事物中，虽然前后不一样，也能够进行识别，发现动物是怎么做到这一点是一个科学挑战。 科学问题可以通过建立模型来解决，更确切的说是建

48、立模拟器，例如人工系统通过学习来粗略地展示具有相同功能的东西，在建立这个系统和取得真实对象相关参数的过程中获得得了对这个事物的理解，这样的系统可以替换原来的对象，甚至可以提高原来的性能，但在其它方面可能是更差。例如，飞机可以飞得比鸟快，但在智能方面却远远不如鸟，然而，我们的研究不是为了达到跟鸟全部一样，更重要的是飞行能力。 通过模仿鸟的飞行可以学到很多飞行方面的技术，但无法学到其精确的分辨能力。通过建立固定不动的翅膀，而不是自由扇动的翅膀，我们知道了怎么飞行。 最后，存在希望逐渐从原来的科学问题中引申出来的工程技术，如在重载下怎么飞得更长时间，怎么减少噪音，慢慢地把注意点转移到其它的知识领域。

49、 上面表明，模式识别(源于观察的抽象和归纳能力)科学研究和应用技术领域中的人工智能模式识别设备设计存在差别，后者不会放过任何相互间互利的因素。注意这里所说的模式在很多层次上是有区分的，就如结构元素的简单特征（如笔画），体现了从在一组个体中表示某一个性质集的个体特征，到综合概念和归纳的特征。一个模式可能表示成一个单独个体，如某个总体、模型或概念的表示。结合模式、规律、特征或性质，模式识别所做的事可以说是在感观测定的低层次上(如图像的象素)，也可以说是在推理和有意义概念的高层层次上(如图像中的人脸)。这里，我们注重在科学研究方面，如模式识别的实现途径是什么，特别是我们在建立人工识别设备需要具备什么

50、技术。 已经有些人在讨论基于模拟和建模尝试的模式识别科学了。在开始的六十年里，其中有个叫Sayre的人做了关于感知器、模式识别和分类的哲学研究，他断言分类方法在某些程度上可以被成功实现，但或许也会失败。根据问题的一些情况我们可以进行模拟识别。虽然我们不能完全识别某个个体，但是我们至少可以根据需要把对象分类出来。识别和分类的Sayre区分方法跟模式识别的两个传统的学习方法有关：无监督学习和有监督学习，这个两个方法可以实现识别和分类方法，科学家利用这个自动化工具来发现自然界中的规律。 无监督学习(也称为试探性分析或聚类分析)：这个方法给研究者一种在观察中自动表示未确定模式(规律)方法，通过这种方法模式种类被确定(检验)了下来，依此，根据Sayre观点，一个模式就可以被被识别出来了。 有监督学习：是这样的一个自动系统，检验(确定)已被研究者通过一种表示方法定义好了的模式，这就是通过评估来实现的