There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Data Mining - Concepts and Techniques.

We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.

From few to many: illumination cone models for face recognition under variable lighting and pose

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

Neural network-based face detection

The human face is an object that is easily located in complex scenes by humans and adults alike. Yet the development of an automated system to perform this task is extremely challenging. This paper is about developing computational procedures to locate human faces in newspaper photographs where scenes are often cluttered making object location non-trivial. On the other hand, the task is made feasible by constraints which follow naturally from rules in photo-journalism. Faces identified by the caption are clearly depicted without occlusion and contrast against the background and sizes of faces fall within a fixed range depending on the dimensions of the photograph and the number of people featuring in it. >

Caption-aided face location in newspaper photographs

This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature point coordinates as unary features and the differences in coordinates between the neighboring feature points as binary features. Each character is modeled as a MRF and word MRFs are constructed by concatenating character MRFs according to a trie lexicon of words during recognition. Our method expands the search space using a character-synchronous beam search strategy to search the segmentation and recognition paths. This method restricts the search paths from the trie lexicon of words and preceding paths, as well as the lengths of feature points during path search. We also combine it with a P2DBMN-MQDF recognizer that is widely used for Chinese and Japanese character recognition.

/pdf/online-handwritten-cursive-word-recognition-using-4hg6y9c03v.pdf

Online Handwritten Cursive Word Recognition Using Segmentation-Free MRF in Combination with P2DBMN-MQDF

Most current on-line signature verification systems are pen-based, i.e., the signature is written by a pen on a digital tablet. To apply signature verification techniques to Internet applications, one desirable way is to use mouse as the input device. In this paper, a mouse based on-line signature verification system is presented. The signature data is represented in a compact and secure way. The enrollment is convenient for users and the verification can be real-time. The system architecture is a simple client-server structure that suits the common internet applications. The client side posts a sequence of X*Y to the server. That is, the raw signature sequence [X,Y ] is represented by X+Y (dot sum of the X-, Y -coordinate sequences). Experimental results show that X+Y is effective for verification. Even if the sequence X+Y is intercepted during transmission over the Internet, original shape of signature [X, Y ] can be recovered. To make the system easy to implement, the client side does nothing complex except posting X+Y to the server. The server side preprocesses the sequence and saves it to database during enrollment or match it against the claimed template during verification. The matching technique adopted here is the similarity measure ER 2. Simulation results show that the mouse based verification system is reliable and secure for internet applications.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Mouse-based signature verification for secure Internet transactions

This paper introduces the novel concept of accent in handwritten text. Accent in speech has been studied by linguists as well as computer scientists, however, it has not been considered in handwriting. We validate this concept in handwriting. We perform our experiments with two sets of Arabic writers. The Native writers are whose first written script is Arabic and Non-Natives that wrote other scripts before learning Arabic as an additional script. Outputs from directional Gabor filters were used as input features to an SVM for classification. We achieved an overall accuracy of 86.96% and 78.54% in two different settings at classifying a given word as being from either class. A perfect classification at a document level was achieved for our dataset using a max-vote scheme.

https://hal.inria.fr/inria-00112630/document

On the Accent in Handwriting of Individuals

In this work, we propose a novel multilingual word spotting framework based on Hidden Markov Models that works on corpus of multilingual handwritten documents and documents that contain more than one handwritten script. The system deals with large multilingual vocabularies without need for word or character segmentation. A keyword is represented by concatenating its character models. We propose and compare two systems: a script identifier based (IDB) and a script identifier free (IDF) system. IDB uses a HMM based script identifier before spotting a keyword. While, IDF does the spotting without the script identification. The system is evaluated on a mixed corpus of public dataset from several scripts such as IAM for English, AMA for Arabic and LAW for Devanagari and on synthetic dataset generated by concatenating words and lines from different scripts in a document image.

Venu Govindaraju

Papers

Caption-aided face location in newspaper photographs

Online Handwritten Cursive Word Recognition Using Segmentation-Free MRF in Combination with P2DBMN-MQDF

Mouse-based signature verification for secure Internet transactions

On the Accent in Handwriting of Individuals

Multilingual word spotting in offline handwritten documents