Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

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Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.

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Reading Digits in Natural Images with Unsupervised Feature Learning

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper presents a new approach to the extraction of straight lines in intensity images. Pixels are grouped into line-support regions of similar gradient orientation, and then the structure of the associated intensity surface is used to determine the location and properties of the edge. The resulting regions and extracted edge parameters form a low-level representation of the intensity variations in the image that can be used for a variety of purposes. The algorithm appears to be more effective than previous techniques for two key reasons: 1) the gradient orientation (rather than gradient magnitude) is used as the initial organizing criterion prior to the extraction of straight lines, and 2) the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements.

Extracting Straight Lines

THE SCHEMA SYSTEM EMBODIES A KNOWLEDGE-BASED APPROACH TO SCENE INTERPRE- TATION. LOW-LEVEL ROUTINES ARE APPLIED TO EXTRACT IMAGE DESCRIPTORS CALLED TOKENS, AND THESE TOKENS ARE FURTHER ORGANIZED BY INTERMEDIATE-LEVEL ROUT- INES INTO MORE ABSTRACT STRUCTURES THAT CAN BE ASSOCIATED WITH OBJECT INST- ANCES. THE THOUSANDS OF TOKENS THAT ARE EXTRACTED FROM AN IMAGE CAN BE GROUPED IN A COMBINATORIALLY EXPLOSIVE MANNER. THEREFORE, KNOWLEDGE IN THE SCHEMA SYSTEM IS NOT LIMITED TO THE DESCRIPTIONS OF OBJECTS; IT INCLUDES INFORMATION ABOUT HOW EACH OBJECT CAN BE RECOGNIZED. OBJECT SCHEMAS CONTROL THE INVOCATION AND EXECUTION OF THE LOW-LEVEL AND INTERMEDIATE-LEVEL ROUT- INES WITH THE GOAL OF FORMING HYPOTHESES ABOUT OBJECTS IN THE SCENE. THE SYSTEM DESCRIBED PRODUCES IMAGE INTERPRETATIONS BASED ON TWO-DIMENSIONAL REASONING, ALTHOUGH NOTHING IN THE SYSTEM ORGANIZATION AND CONTROL STRATEG- IES PRECLUDE THE INCLUSION OF THREE-DIMENSIONAL INFORMATION. THE SCHEMA FRAMEWORK EXPLOITS COARSE-GRAINED PARALLELISM IN A COOPERA- TIVE INTERPRETATION PROCESS. SCHEMA INSTANCES RUN CONCURRENTLY, AND AN OB- JECT SCHEMA OFTEN HAS AVAILABLE A VARIETY OF STRATEGIES FOR IDENTIFICATION, EACH ONE INVOKING KNOWLEDGE SOURCES TO GATHER SUPPORT FOR THE PRESENCE OF A HYPOTHESIZED OBJECT. INTER-SCHEMA COMMUNICATION IS CARRIED OUT ASYNCHRON- OUSLY THROUGH A GLOBAL BLACKBOARD. IN THIS WAY SCHEMA INSTANCES COOPERATE TO IDENTIFY AND LOCATE THE SIGNIFICANT OBJECTS PRESENT IN THE SCENE.

The Schema System

THIS PAPER PROVIDES AN OVERVIEW OF THE IMAGE UNDERSTANDING ARCHITECTURE (IUA), A MASSIVELY PARALLEL, MULTI-LEVEL SYSTEM FOR SUPPORTING REAL-TIME IMAGE UNDERSTANDING APPLICATIONS AND RESEARCH IN KNOWLEDGE-BASED COMPUTER VISION. THE DESIGN OF THE IUA IS MOTIVATED BY CONSIDERING THE ARCHITECTURAL REQUIREMENTS FOR INTEGRATED REAL-TIME VISION IN TERMS OF THE TYPE OF PRO- CESSING ELEMENTS, CONTROL OF PROCESSING, AND COMMUNICATION BETWEEN PROCES- SING ELEMENTS. THE IUA INTEGRATES PARALLEL PROCESSORS OPERATING SIMULTANEOUSLY AT THREE LEVELS OF COMPUTATIONAL GRANULARITY IN A TIGHTLY-COUPLED ARCHITECTURE. EACH LEVEL OF THE IUA IS A PARALLEL PROCESSOR THAT IS DISTINCTLY DIFFERENT FROM THE OTHER TWO LEVELS, IN ORDER TO BEST MEET THE PROCESSING NEEDS AT EACH OF THE CORRESPONDING LEVELS OF ABSTRACTION IN THE INTERPRETATION PROCESS. COM- MUNICATION BETWEEN LEVELS TAKS PLACE VIA PARALLEL DATA AND CONTROL PATHS. THE PROCESSING ELEMENTS WITHIN EACH LEVEL CAN ALSO COMMUNICATE WITH EACH OTHER IN PARALLEL, VIA A DIFFERENT MECHANISM AT EACH LEVEL THAT IS DESIGNED TO MEET THE SPECIFIC COMMUNICATION NEEDS OF EACH LEVEL OF ABSTRACTION. AN ASSOCIATIVE PROCESSING PARADIGM, THAT PROVIDES A SIMPLE YET GENERAL MEANS OF MANAGING MASSIVE PARALLELISM, HAS BEEN UTILIZED AS THE PRINCIPLE CONTROL MECHANISM AT THE LOW AND INTERMEDIATE LEVELS. CONTROL OF PROCESSING IN THESE LEVELS IS BASED UPON THEIR RAPID RESPONSES TO QUERIES, INVOLVING

The Image Understanding Architecture

Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and storefronts Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions Much information available to solve this problem is frequently ignored or used sequentially Similarity between character images is often overlooked as useful information Because of language priors, a recognizer may assign different labels to identical characters Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label Lexicons improve recognition accuracy but are used post hoc We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19 percent, the lexicon reduces word recognition error by 35 percent, and sparse belief propagation reduces the lexicon words considered by 999 percent with a 12X speedup and no loss in accuracy

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Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

THIS PAPER DESCRIBES A WORKING SYSTEM FOR SEGMENTING IMAGES OF COMPLEX SCENES. THE SYSTEM HAS EVOLVED OUT OF MANY YEARS OF RESEARCH IN LOW-LEVEL IMAGE SEGMENTATION AT THE UNIVERSITY OF MASSACHUSETTS. SEGMENTATIONS PRODUCED BY THE SYSTEM ARE USED EXTENSIVELY IN THE IMAGE INTERPRETATION WORK HERE. THE SYSTEM FIRST PRODUCES SEGMENTATIONS BASED UPON AN ANALYSIS OF SPATIALLY LOCALIZED FEATURE HISTOGRAMS. THESE SEGMENTATIONS ARE THEN POST-PROCESSED WITH A REGION MERGING ALGORITHM. A SIMPLE EXTENSION OF THE LOCAL HISTOGRAM ALGORITHM TO MULTIPLE FEATURES IS PRESENTED. ISSUES OF PARAMETER SELECTION FOR THE LOCAL HISTOGRAM SEGMENTATION ALGORITHM ARE ADDRESSED BY PRESENTING A MAPPING FROM THE MULTI-DIMENSIONAL PARAMETER SPACE TO A ONE DIMENSIONAL `SENSITIVITY'' SPACE. RESULTS ARE INCLUDED WHICH DEMONSTRATE THE ROBUST CHARACTER OF THE ALGORITHMS WHEN APPLIED TO OUTDOOR AND AERIAL IMAGERY.

Allen R. Hanson

Papers

Extracting Straight Lines

The Schema System

The Image Understanding Architecture

Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

Segmenting Images Using Localized Histograms and Region Merging