模式识别教程：大师级指南

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"模式识别经典教程是一本由国外大师编写的关于模式识别的教程，具有很高的学习价值。书中可能涵盖了模式识别的基础理论、方法和应用，适合对模式识别感兴趣的学者和学生参考。教程中可能涉及了MATLAB、Simulink以及C++等编程语言在模式识别中的应用，读者可以通过作者的博客获取相关的编程援助。此外，作者提供了QQ、Email和MSN等联系方式，以便解答读者在学习过程中遇到的问题。" 模式识别是人工智能领域的一个重要分支，主要研究如何在给定的信息中自动识别出某种规律或模式。在这个经典教程中，可能会讲解以下关键知识点： 1. **模式识别的基本概念**：包括模式的定义、分类、特征提取和识别过程等基础知识。 2. **统计模式识别**：可能讲解基于概率模型的贝叶斯决策理论、高斯混合模型、朴素贝叶斯分类器等内容。 3. **机器学习基础**：可能涵盖监督学习、无监督学习、半监督学习和强化学习的基本思想，如线性回归、逻辑回归、支持向量机（SVM）、神经网络和深度学习等。 4. **特征提取与选择**：介绍如何从原始数据中提取有意义的特征，如主成分分析（PCA）、线性判别分析（LDA）等，并讨论特征选择的重要性。 5. **图像处理与计算机视觉**：如果教程深入，可能会涉及图像处理技术，如滤波、边缘检测、模板匹配等，以及在模式识别中的应用。 6. **模式识别算法**：可能介绍一些经典算法，如K近邻（KNN）、决策树、随机森林、集成学习等。 7. **模式识别的应用**：包括语音识别、文本分类、生物特征识别（如指纹、人脸识别）、医学图像分析、视频内容理解等多个领域的实例。 8. **MATLAB、Simulink和C++在模式识别中的应用**：教程可能包含使用这些工具进行模式识别建模和实现的指导，帮助读者掌握实际操作技巧。通过这本教程，读者不仅能够系统地学习模式识别的理论知识，还能获得实践编程的经验，提升解决实际问题的能力。同时，作者提供的在线支持也使得学习过程更加顺畅，便于解决遇到的难题。

“03-Ch01-SA272” 17/9/2008 page 1

CHAPTER

Introduction

1.1 IS PATTERN RECOGNITION IMPORTANT?

Pattern recognition is the scientiﬁc discipline whose goal is the classiﬁcation of

objects into a number of categories or classes. Depending on the application, these

objects can be images or signal waveforms or any type of measurements that need

to be classiﬁed. We will refer to these objects using the generic term patterns.

Pattern recognition has a long history,but before the 1960s it was mostly the output

of theoretical research in the area of statistics. As with everything else, the advent

of computers increased the demand for practical applications of pattern recogni-

tion, which in tur n set new demands for further theoretical developments. As our

society evolves from the industrial to its postindustrial phase, automation in indus-

trial production and the need for information handling and retrieval are becoming

increasingly important. This trend has pushed pattern recognition to the high edge

of today’s engineering applications and research. Pattern recognition is an integral

part of most machine intelligence systems built for decision making.

Machine vision is an area in which pattern recognition is of importance.

A machine vision system captures images via a camera and analyzes them to produce

descriptions of what is imaged. A typical application of a machine vision system is

in the manufacturing industry, either for automated visual inspection or for automa-

tion in the assembly line. For example, in inspection, manufactured objects on a

moving conveyor may pass the inspection station, where the camera stands, and it

has to be ascertained whether there is a defect. Thus, images have to be analyzed

online, and a pattern recognition system has to classify the objects into the “defect”

or“nondefect”class. After that,an action has to be taken,such as to reject the offend-

ing parts. In an assembly line, different objects must be located and “recognized,”

that is, classiﬁed in one of a number of classes known a priori. Examples are the

“screwdriver class,” the “German key class,” and so forth in a tools’ manufacturing

unit. Then a robot arm can move the objects in the right place.

Character (letter or number) recognition is another important area of pattern

recognition,with major implications in automation and information handling. Opti-

cal character recognition (OCR) systems are already commercially available and

more or less familiar to all of us. An OCR system has a“front-end”device consisting

of a light source,a scan lens,a document transport,and a detector. At the output of

“03-Ch01-SA272” 17/9/2008 page 2

2 CHAPTER 1 Introduction

the light-sensitive detector, light-intensity variation is translated into “numbers” and

an image array is formed. In the sequel,a series of image processing techniques are

applied leading to line and character segmentation. The pattern recognition soft-

ware then takes over to recognize the characters—that is, to classify each character

in the correct“letter, number, punctuation”class. Storing the recognized document

has a twofold advantage over storing its scanned image. First, further electronic

processing, if needed, is easy via a word processor, and second, it is much more

efﬁcient to store ASCII characters than a document image. Besides the printed

character recognition systems, there is a great deal of interest invested in systems

that recognize handwriting. A typical commercial application of such a system is

in the machine reading of bank checks. The machine must be able to recognize

the amounts in ﬁgures and digits and match them. Furthermore, it could check

whether the payee corresponds to the account to be credited. Even if only half of

the checks are manipulated cor rectly by such a machine, much labor can be saved

from a tedious job. Another application is in automatic mail-sorting machines for

postal code identiﬁcation in post ofﬁces. Online handwriting recognition systems

are another area of great commercial interest. Such systems will accompany pen

computers, with which the entry of data will be done not via the keyboard but by

writing. This complies with today’s tendency to develop machines and computers

with interfaces acquiring human-like skills.

Computer-aided diagnosis is another important application of pattern recogni-

tion, aiming at assisting doctors in making diagnostic decisions. The ﬁnal diagnosis

is, of course, made by the doctor. Computer-assisted diagnosis has been applied to

and is of interest for a variety of medical data,such as X-rays,computed tomographic

images, ultrasound images, electrocardiograms (ECGs), and electroencephalograms

(EEGs). The need for a computer-aided diagnosis stems from the fact that medi-

cal data are often not easily interpretable, and the interpretation can depend very

much on the skill of the doctor. Let us take for example X-ray mammography

for the detection of breast cancer. Although mammography is currently the best

method for detecting breast cancer, 10 to 30% of women who have the disease and

undergo mammography have negative mammograms. In approximately two thirds

of these cases with false results the radiologist failed to detect the cancer, which

was evident retrospectively. This may be due to poor image quality, eye fatigue

of the radiologist, or the subtle nature of the ﬁndings. The percentage of correct

classiﬁcations improves at a second reading by another radiologist. Thus, one can

aim to develop a pattern recognition system in order to assist radiologists with a

“second” opinion. Increasing conﬁdence in the diagnosis based on mammograms

would, in turn, decrease the number of patients with suspected breast cancer who

have to undergo surgical breast biopsy, with its associated complications.

Speech recognition is another area in which a great deal of research and devel-

opment effort has been invested. Speech is the most natural means by which

humans communicate and exchange information. Thus, the goal of building intelli-

gent machines that recognize spoken information has been a long-standing one for

scientists and engineers as well as science ﬁction writers. Potential applications of

such machines are numerous. They can be used, for example,to improve efﬁciency

“03-Ch01-SA272” 17/9/2008 page 3

1.1 Is Pattern Recognition Important? 3

in a manufacturing environment, to control machines in hazardous environments

remotely, and to help handicapped people to control machines by talking to them.

A major effort, which has already had considerable success, is to enter data into

a computer via a microphone. Software, built around a pattern (spoken sounds

in this case) recognition system, recognizes the spoken text and translates it into

ASCII characters,which are shown on the screen and can be stored in the memory.

Entering information by “talking” to a computer is twice as fast as entry by a skilled

typist. Furthermore, this can enhance our ability to communicate with deaf and

dumb people.

Data mining and knowledge discovery in databases is another key application

area of pattern recognition. Data mining is of intense interest in a wide range of

applications such as medicine and biology, market and ﬁnancial analysis, business

management, science exploration, image and music retrieval. Its popularity stems

from the fact that in the age of information and knowledge society there is an ever

increasing demand for retrieving information and turning it into knowledge. More-

over,this information exists in huge amounts of data in various forms including,text,

images, audio and video, stored in different places distributed all over the world.

The traditional way of searching information in databases was the description-based

model where object retrieval was based on keyword description and subsequent

word matching. However, this type of searching presupposes that a manual anno-

tation of the stored information has previously been performed by a human. This

is a very time-consuming job and, although feasible when the size of the stored

information is limited, it is not possible when the amount of the available informa-

tion becomes large. Moreover, the task of manual annotation becomes problematic

when the stored information is widely distributed and shared by a heterogeneous

“mixture”of sites and users. Content-based retrieval systems are becoming more and

more popular where information is sought based on “similarity”between an object,

which is presented into the system, and objects stored in sites all over the world.

In a content-based image retrieval CBIR (system) an image is presented to an input

device (e.g.,scanner). The system returns“similar”images based on a measured“sig-

nature,” which can encode, for example, information related to color, texture and

shape. In a music content-based retrieval system, an example (i.e., an extract from

a music piece), is presented to a microphone input device and the system returns

“similar” music pieces. In this case, similarity is based on certain (automatically)

measured cues that characterize a music piece, such as the music meter, the music

tempo, and the location of certain repeated patterns.

Mining for biomedical and DNA data analysis has enjoyed an explosive growth

since the mid-1990s. All DNA sequences comprise four basic building elements;

the nucleotides: adenine (A), cytosine (C), guanine (G) and thymine (T). Like the

letters in our alphabets and the seven notes in music, these four nucleotides are

combined to form long sequences in a twisted ladder form. Genes consist of,usually,

hundreds of nucleotides arranged in a particular order. Speciﬁc gene-sequence

patterns are related to particular diseases and play an important role in medicine.

To this end, pattern recognition is a key area that offers a wealth of developed tools

for similarity search and comparison between DNA sequences. Such comparisons

“03-Ch01-SA272” 17/9/2008 page 5

1.2 Features, Feature Vectors, and Classiﬁers 5

␮

␴

⫹

FIGURE 1.2

Plot of the mean value versus the standard deviation for a number of different images originating

from class A (



) and class B (⫹). In this case, a straight line separates the two classes.

with a number of patterns, some of which are known to originate from class A and

some from class B.

The ﬁrst step is to identify the measurable quantities that make these two regions

distinct from each other. Figure 1.2 shows a plot of the mean value of the inten-

sity in each region of interest versus the corresponding standard deviation around

this mean. Each point corresponds to a different image from the available database.

It turns out that class A patterns tend to spread in a different area from class B pat-

terns. The straight line seems to be a good candidate for separating the two classes.

Let us now assume that we are given a new image with a region in it and that we

do not know to which class it belongs. It is reasonable to say that we measure the

mean intensity and standard deviation in the region of interest and we plot the cor-

responding point. This is shown by the asterisk (∗) in Figure 1.2. Then it is sensible

to assume that the unknown pattern is more likely to belong to class A than class B.

The preceding artiﬁcial classiﬁcation task has outlined the rationale behind a

large class of pattern recognition problems. The measurements used for the classiﬁ-

cation,the mean value and the standard deviation in this case,are known as features.

In the more general case l features x

, i ⫽ 1, 2, ..., l, are used, and they form the

feature vector

x ⫽ [x

, x

, ..., x

]

where T denotes transposition. Each of the feature vectors identiﬁes uniquely

a single pattern (object). Throughout this book features and feature vectors will

be treated as random variables and vectors, respectively. This is natural, as the

measurements resulting from dif ferent patterns exhibit a random variation. This

is due partly to the measurement noise of the measuring devices and partly to

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