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2011-08-03 08:48:50|  分类: 默认分类 |  标签: |举报 |字号 订阅

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2 Four Approaches to Pattern Recognition

2 模式识别四种方法


In science, new knowledge is phrased in terms of existing knowledge. The starting point of this process is set by generally accepted evident views, or observations and facts that cannot be explained further. These foundations,however, are not the same for all researchers. Different types of approaches may be distinguished originating from different starting positions. It is almost a type of taste from which perspective a particular researcher begins. As a consequence, different ‘schools’ may arise. The point of view, however, determines what we see. In other words, staying within a particular framework of thought we cannot achieve more than what is derived as a consequence of the corresponding assumptions and constraints. To create more complete and objective methods, we may try to integrate scientific results originating from different approaches into a single pattern recognition model. It is possible that confusion arises on how these results may be combined and where they essentially differ. But the combination of results of different approaches may also appear to be fruitful, not only for some applications, but also for the scientific understanding of the researcher that broadens the horizon of allowable starting points. This step towards a unified or integrated view is very important in science as only then a more complete understanding is gained or a whole theory is built.



Below we will describe four approaches to pattern recognition which arise from two different dichotomies of the starting points. Next, we will present some examples illustrating the difficulties of their possible interactions. This discussion is based on earlier publications [16, 17].



2.1 Platonic and Aristotelian Viewpoints

2.1 柏拉图和亚里士多德观点


Two principally different approaches to almost any scientific field rely on the so-called Platonic and Aristotelian viewpoints. In a first attempt they may be understood as top-down and bottom-up ways of building knowledge. They are also related to deductive (or holistic) and inductive (or reductionistic) principles. These aspects will be discussed in Section 4.



The Platonic approach starts from generally accepted concepts and global ideas of the world. They constitute a coherent picture in which many details are undefined. The primary task of the Platonic researcher is to recognize in his observations the underlying concepts and ideas that are already accepted by him. Many theories of the creation of the universe or the world rely on this scenario. An example is the drifts of the continents or the extinction of the mammoths. These theories do not result from a reasoning based on observations, but merely from a more or less convincing global theory (depending on the listener!) that seems to extrapolate far beyond the hard facts. For the Platonic researcher, however, it is not an extrapolation, but an adaptation of previous formulations of the theory to new facts. That is the way this approach works: existing ideas that have been used for a long time are gradually adapted to new incoming observations. The change does not rely on an essential paradigm shift in the concept, but on finding better, more appropriate relations with the observed world in definitions and explanations. The essence of the theory has been constant for a long time. So, in practise the Platonic researcher starts from a theory which can be stratified into to a number of hypotheses that can be tested. Observations are collected to test these hypotheses and, finally, if the results are positive, the theory is confirmed.



The observations are of primary interest in the Aristotelian approach. Scientific reasoning stays as closely as possible to them. It is avoided to speculate on large, global theories that go beyond the facts. The observations are always the foundation on which the researcher builds his knowledge. Based on them,patterns and regularities are detected or discovered, which are used to formulate some tentative hypotheses. These are further explored in order to arrive at general conclusions or theories. As such, the theories are not global, nor do they constitute high level descriptions. A famous guideline here is the socalled Occam’s razor principle that urges one to avoid theories that are more complex than strictly needed for explaining the observations. Arguments may arise, however, since the definition of complexity depends, e.g. on the mathematical formalism that is used.



The choice for a particular approach may be a matter of preference or determined by non-scientific grounds, such as upbringing. Nobody can judge what the basic truth is for somebody else. Against the Aristotelians may be held that they do not see the overall picture. The Platonic researchers, on the other hand, may be blamed for building castles in the air. Discussions between followers of these two approaches can be painful as well as fruitful.They may not see that their ground truths are different, leading to pointless debates. What is more important is the fact that they may become inspired by each other’s views. One may finally see real world examples of his concepts,while the other may embrace a concept that summarizes, or constitutes an abstraction of his observations.



2.2 Internal and the External Observations

2.2 内在的和外在的观察


In the contemporary view science is ‘the observation, identification, description,experimental investigation, and theoretical explanation of phenomena’or ‘any system of knowledge that is concerned with the physical world and its phenomena and that entails unbiased observations and systematic experimentation. So, the aspect of observation that leads to a possible formation of a concept or theory is very important. Consequently, the research topic of the science of pattern recognition, which aims at the generalization from observations for knowledge building, is indeed scientific. Science is in the end a brief explanation summarizing the observations achieved through abstraction and their generalization.



Such an explanation may primarily be observed by the researcher in his own thinking. Pattern recognition research can thereby be performed by introspection. The researcher inspects himself how he generalizes from observations. The basis of this generalization is constituted by the primary observations. This may be an entire object (‘I just see that it is an apple’)or its attributes (‘it is an apple because of its color and shape’). We can also observe pattern recognition in action by observing other human beings(or animals) while they perform a pattern recognition task, e.g. when they recognize an apple. Now the researcher tries to find out by experiments and measurements how the subject decides for an apple on the basis of the stimuli presented to the senses. He thereby builds a model of the subject, from senses to decision making.



Both approaches result into a model. In the external approach, however,the senses may be included in the model. In the internal approach, this is either not possible or just very partially. We are usually not aware of what happens in our senses. Introspection thereby starts by what they offer to our thinking(and reasoning). As a consequence, models based on the internal approach have to be externally equipped with (artificial) senses, i.e. with sensors.



2.3 The Four Approaches

2.3 四种模式识别方法


The following four approaches can be distinguished by combining the two

dichotomies presented above:


(1) Introspection by a Platonic viewpoint: object modeling.

(2) Introspection by an Aristotelian viewpoint: generalization.

(3) Extrospection by an Aristotelian viewpoint: system modeling.

(4) Extrospection by a Platonic viewpoint: concept modeling.







These four approaches will now be discussed separately. We will identify some

known procedures and techniques that may be related to these. See also Fig. 2.


Object modeling. This is based on introspection from a Platonic viewpoint.The researcher thereby starts from global ideas on how pattern recognition systems may work and tries to verify them in his own thinking and reasoning.He thereby may find, for instance, that particular color and shape descriptions of an object are sufficient for him to classify it as an apple. More generally, he may discover that he uses particular reasoning rules operating on a fixed set of possible observations. The so-called syntactic and structural approaches to pattern recognition [26] thereby belong to this area, as well as the case-based reasoning [3]. There are two important problems in this domain: how to constitute the general concept of a class from individual object descriptions and how to connect particular human qualitative observations such as ‘sharp edge’or ‘egg shaped’ with physical sensor measurements.



Generalization. Let us leave the Platonic viewpoint and consider a researcher who starts from observations, but still relies on introspection. He wonders what he should do with just a set of observations without any framework.An important point is the nature of observations. Qualitative observations such as ‘round’, ‘egg-shaped’ or ‘gold colored’ can be judged as recognitions in themselves based on low-level outcomes of senses. It is difficult to neglect them and to access the outcomes of senses directly. One possibility for him is to use artificial senses, i.e. of sensors, which will produce quantitative descriptions. The next problem, however, is how to generalize from such numerical outcomes. The physiological process is internally unaccessible. A researcher who wonders how he himself generalizes from low level observations given by numbers may rely on statistics. This approach thereby includes the area of statistical pattern recognition.If we consider low-level inputs that are not numerical, but expressed in attributed observations as ‘red, egg-shaped’, then the generalization may be based on logical or grammatical inference. As soon, however, as the structure of objects or attributes is not generated from the observations, but derived (postulated) from a formal global description of the application knowledge,e.g. by using graph matching, the approach is effectively top-down and thereby starts from object or concept modeling.



System modeling. We now leave the internal platform and concentrate on research that is based on the external study of the pattern recognition abilities of humans and animals or their brains and senses. If this is done in a bottom-up way, the Aristotelian approach, then we are in the area of low level modeling of senses, nerves and possibly brains. These models are based on the physical and physiological knowledge of cells and the proteins and minerals that constitute them. Senses themselves usually do not directly generalize from observations. They may be constructed, however, in such a way that this process is strongly favored on a higher level. For instance, the way the eye (and the retina, in particular) is constructed, is advantageous for the detection of edges and movements as well as for finding interesting details in a global, overall picture. The area of vision thereby profits from this approach. It is studied how nerves process the signals they receive from the senses on a level close to the brain. Somehow this is combined towards a generalization of what is observed by the senses. Models of systems of multiple nerves are called neural networks. They appeared to have a good generalization ability and are thereby also used in technical pattern recognition applications in which the physiological origin is not relevant [4, 62].



Concept modeling. In the external platform, the observations in the starting point are replaced by ideas and concepts. Here one still tries to externally model the given pattern recognition systems, but now in a top-down manner.



An example is the field of expert systems: by interviewing experts in a particular pattern recognition task, it is attempted to investigate what rules they use and in what way they are using observations. Also belief networks and probabilistic networks belong to this area as far as they are defined by experts and not learned from observations. This approach can be distinguished from the above system modeling by the fact that it is in no way attempted to model a physical or physiological system in a realistic way. The building blocks are the ideas, concepts and rules, as they live in the mind of the researcher. They are adapted to the application by external inspection of an expert, e.g. by interviewing him. If this is done by the researcher internally by introspection,we have closed the circle and are back to what we have called object modeling,as the individual observations are our internal starting point. We admit that the difference between the two Platonic approaches is minor here (in contrast to the physiological level) as we can also try to interview ourselves to create an objective (!) model of our own concept definitions.



2.4 Examples of Interaction

2.4 四种方法交叉运用的例子

The four presented approaches are four ways to study the science of pattern recognition. Resulting knowledge is valid for those who share the same starting point. If the results are used for building artificial pattern recognition devices, then there is, of course, no reason to restrict oneself to a particular approach. Any model that works well may be considered. There are, however,certain difficulties in combining different approaches. These may be caused by differences in culture, assumptions or targets. We will present two examples,one for each of the two dichotomies.



Artificial neural networks constitute an alternative technique to be used for generalization within the area of statistical pattern recognition. It has taken, however, almost ten years since their introduction around 1985 before neural networks were fully acknowledged in this field. In that period, the neural network community suffered from lack of knowledge on the competing classification procedures. One of the basic misunderstandings in the pattern recognition field was caused by its dominating paradigm stating that learning systems should never be larger than strictly necessary, following the Occam’s razor principle. It could have not been understood how largely oversized systems such as neural networks would have ever been able to generalize without adapting to peculiarities in the data (the so-called overtraining). At the same time, it was evident in the neural network community that the larger neural network the larger its flexibility, following the analogy that a brain with many neurons would perform better in learning than a brain with a few ones. When this contradiction was finally solved (an example of Kuhn’s paradigm shifts [48]), the area of statistical pattern recognition was enriched with a new set of tools. Moreover, some principles were formulated towards understanding of pattern recognition that otherwise would have only been found with great difficulties.



In general, it may be expected that the internal approach profits from the results in the external world. It is possible that thinking, the way we generalize from observations, changes after it is established how this works in nature.For instance, once we have learned how a specific expert solves his problems,this may be used more generally and thereby becomes a rule in structural pattern recognition. The external platform may thereby be used to enrich the internal one.



A direct formal fertilization between the Platonic and Aristotelian approaches is more difficult to achieve. Individual researchers may build some understanding from studying each other’s insights, and thereby become mutually inspired. The Platonist may become aware of realizations of his ideas and concepts. The Aristotelian may see some possible generalizations of the observations he collected. It is, however, still one of the major challenges in science to formalize this process.



How should existing knowledge be formulated such that it can be enriched by new observations? Everybody who tries to do this directly encounters the problem that observations may be used to reduce uncertainty (e.g. by the parameter estimation in a model), but that it is very difficult to formalize uncertainty in existing knowledge. Here we encounter a fundamental ‘paradox’for a researcher summarizing his findings after years of observations and studies: he has found some answers, but almost always he has also generated more new questions. Growing knowledge comes with more questions. In any formal system, however, in which we manage to incorporate uncertainty(which is already very difficult), this uncertainty will be reduced after having incorporating some observations.We need an automatic hypothesis generation in order to generate new questions. How should the most likely ones be determined? We need to look from different perspectives in order to stimulate the creative process and bring sufficient inspiration and novelty to hypothesis generation. This is necessary in order to make a step towards building a complete theory. This, however, results in the computational complexity mentioned in the literature [60] when the Platonic structural approach to pattern recognition has to be integrated with the Aristotelian statistical approach.



The same problem may also be phrased differently: how can we express the uncertainty in higher level knowledge in such a way that it may be changed (upgraded) by low level observations? Knowledge is very often structural and has thereby a qualitative nature. On the lowest level, however, observations are often treated as quantities, certainly in automatic systems equipped with physical sensors. And here the Platonic – Aristotelian polarity meets the internal– external polarity: by crossing the border between concepts and observations we also encounter the border between qualitative symbolic descriptions and quantitative measurements.


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