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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

CA : A Cancer Journal for Clinicians

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

This tutorial was already accepted for last years ICMR but then it was unfortunately decided not to run tutorials at the conference, so I am hoping to give it at this years VCIP instead.

Proposal for VCIP 2012 tutorial on “image database visualisation and browsing”

While content-based image retrieval (CBIR) has been an active research area for more than two decades, the computational overhead associated with image feature extraction is often high, making existing methods unsuitable for on-line retrieval where image features need to be extracted during the retrieval process. In this paper, we present an image retrieval algorithm for JPEG images that works in an extremely fast fashion, and is based solely on information contained in the file headers. In particular, we demonstrate that optimising the Huffman tables of JPEG files not only leads to improved compression but also allows retrieval based on the (image adapted) Huffman tables. Exploiting this leads to a retrieval method that is about 40 times faster than existing compressed domain algorithms and at least 150 times faster than common pixel-domain methods. While retrieval performance on its own does not quite match that of current techniques, the method is shown to work well as an image filter to discard a large part of a database in an efficient way. Combined with a more accurate compressed-domain retrieval algorithm it is found that retrieval time can be shortened by about 80% without sacrificing retrieval accuracy.

Sorting JPEG images at a glance

Hiding multiple images into a single image via joint compressive autoencoders

This paper presents the results of a comprehensive analysis of structural changes in the caudal vertebrae of Turner's thick-toed geckos by computer microtomography and X-ray fluorescence analysis. We present algorithms used for the reconstruction of tomographic images which allow to work with high noise level projections that represent typical conditions dictated by the nature of the samples. Reptiles, due to their ruggedness, small size, belonging to the amniote and a number of other valuable features, are an attractive model object for long-orbital experiments on unmanned spacecraft. Issues of possible changes in their bone tissue under the influence of spaceflight are the subject of discussions between biologists from different laboratories around the world.

Computed microtomography and x-ray fluorescence analysis for comprehensive analysis of structural changes in bone

Efficient and effective techniques for managing and browsing large image databases are increasingly sought after. This chapter presents a simple yet efficient and effective approach to navigating image datasets. Based on the concept of a globe as visualisation and navigation medium, thumbnails are projected onto the surface of a sphere based on their colour. Navigation is performed by rotating and tilting the globe as well as zooming into an area of interest. Experiments based on a medium size image database demonstrate the usefulness of the presented approach. © 2009, IGI Global.

Gerald Schaefer

Papers

Proposal for VCIP 2012 tutorial on “image database visualisation and browsing”

Sorting JPEG images at a glance

Hiding multiple images into a single image via joint compressive autoencoders

Computed microtomography and x-ray fluorescence analysis for comprehensive analysis of structural changes in bone

Intuitive image database navigation by hue-sphere browsing