Download or read book Metric Learning Applied for Automatic Large Scale Image Classification written by Sahilu Wendeson Sahilu and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current Internet world, the numbers of digital images are growing exponentially. As a result, it is very tough to retrieve relevant objects for a given query point. For the past few years, researchers have been contributing different algorithms in the two most common machine learning categories to either cluster or classify images. There are several techniques of supervised classification images depending on the local or global feature representation of images, and on the metric used to calculate the distance (or similarity) between images. Recently many studies have shown the interest to learn a metric rather than use a simple metric a priori (e.g. Euclidean distance). This approach is described in the literature as metric learning. The main objective of this thesis is to use metric learning algorithm in the context of large-scale image classification. In this project, we use a metric learning algorithm which is driven by the nearest neighbors approach and has a competence to improve the generic k Nearest Neighbor (kNN) machine learning algorithm. Even though we get significant improvement on the performance of classification, the computation is very expensive due to the large dimensionality of our input dataset. Thus, we use the dimension reduction technique to reduce dimension and computation time as well. Nevertheless, due to the size of the database, classifying and searching a given query point using metric learning algorithm alone exhaustively is intractable.
Download or read book Computer Vision ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Download or read book Metric Learning written by Aurélien Muise and published by Springer Nature. This book was released on 2022-05-31 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies
Download or read book Metric Learning written by Brian Kulis and published by Now Pub. This book was released on 2013 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metric Learning: A Review presents an overview of existing research in metric learning, including recent progress on scaling to high-dimensional feature spaces and to data sets with an extremely large number of data points. It presents as unified a framework as possible under which existing research on metric learning can be cast.
Download or read book Large scale High Dimensional Distance Metric Learning and Its Application to Computer Vision written by Qi Qian and published by . This book was released on 2015 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics written by Le Lu and published by Springer Nature. This book was released on 2019-09-19 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Download or read book An Introduction to Optimization written by Edwin K. P. Chong and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern, up-to-date introduction to optimization theory and methods This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization. Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides: * A review of the required mathematical background material * A mathematical discussion at a level accessible to MBA and business students * A treatment of both linear and nonlinear programming * An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods * A chapter on the use of descent algorithms for the training of feedforward neural networks * Exercise problems after every chapter, many new to this edition * MATLAB(r) exercises and examples * Accompanying Instructor's Solutions Manual available on request An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Download or read book Genetic Programming for Image Classification written by Ying Bi and published by Springer Nature. This book was released on 2021-02-08 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
Download or read book Metric Learning and Scale Estimation in High Dimensional Machine Learning Problems with an Application to Generic Object Recognition written by Nayyar Abbas Zaidi and published by . This book was released on 2011 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: A typical machine learning algorithm takes advantage of training data to discover patterns among observed variables. For example, a learning algorithm can predict the label of a test data point by measuring its similarity with the training data points. Since the data is constituted of features which are not necessarily related by any evident relation, the notion of similarity is not trivially defined. When measuring similarity, one should not necessarily treat all features as being equally important. The problem of learning in high-dimensional machine learning data is actually the problem of estimating the relative importance of the features. The relevance determination tunes a data-dependent similarity measure. Most machine learning algorithms either implicitly or explicitly learn this similarity measure on which their performance is critically dependent. In this thesis, various metric learning techniques are analyzed and systematically studied under a unified framework to highlight the criticality of data-dependent distance metric in machine learning. The metric learning algorithms are categorized as naive, semi-naive, complete and high-level metric learning, under a common distance measurement framework. The connection of feature selection, feature weighting, feature partitioning, kernel tuning, etc. with metric learning is discussed and it is shown that they are all in fact forms of metric learning. Novel naive, semi-naive, complete and high-level metric learning algorithms are proposedto improve classification performance. Also, it has been shown that the realm of metric learning is not limited to k-nearest neighbor classification, and that a metric optimized in the k-nearest neighbor setting is likely to be effective and applicable in other kernel-based frameworks, for example Support Vector Machines (SVM) and Gaussian Processes (GP).
Download or read book Computer Vision ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 910 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
Download or read book Deep Learning for Marine Science written by Haiyong Zheng and published by Frontiers Media SA. This book was released on 2024-05-15 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.
Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-10-06 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Download or read book Advances in Visual Computing written by George Bebis and published by Springer. This book was released on 2015-12-17 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 9474 and LNCS 9475 constitutes the refereed proceedings of the 11th International Symposium on Visual Computing, ISVC 2015, held in Las Vegas, NV, USA in December 2015. The 115 revised full papers and 35 poster papers presented in this book were carefully reviewed and selected from 260 submissions. The papers are organized in topical sections: Part I (LNCS 9474) comprises computational bioimaging; computer graphics; motion and tracking; segmentation; recognition; visualization; mapping; modeling and surface reconstruction; advancing autonomy for aerial robotics; medical imaging; virtual reality; observing humans; spectral imaging and processing; intelligent transportation systems; visual perception and robotic systems. Part II (LNCS 9475): applications; 3D computer vision; computer graphics; segmentation; biometrics; pattern recognition; recognition; and virtual reality.
Download or read book Pattern Recognition and Computer Vision written by Shiqi Yu and published by Springer Nature. This book was released on 2022-10-27 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022. The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking.
Download or read book Computer Vision for Microscopy Image Analysis written by Mei Chen and published by Academic Press. This book was released on 2020-12-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation
Download or read book Advances in Multimedia Information Processing PCM 2017 written by Bing Zeng and published by Springer. This book was released on 2018-05-09 with total page 1029 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 10735 and 10736 constitutes the thoroughly refereed proceedings of the 18th Pacific-Rim Conference on Multimedia, PCM 2017, held in Harbin, China, in September 2017. The 184 full papers presented were carefully reviewed and selected from 264 submissions. The papers are organized in topical sections on: Best Paper Candidate; Video Coding; Image Super-resolution, Debluring, and Dehazing; Person Identity and Emotion; Tracking and Action Recognition; Detection and Classification; Multimedia Signal Reconstruction and Recovery; Text and Line Detection/Recognition; Social Media; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing.
Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-06 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
