Learning Structured Prediction Models In Computer Vision
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Structured Learning And Prediction In Computer Vision
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Author : Sebastian Nowozin
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Structured Learning And Prediction In Computer Vision written by Sebastian Nowozin and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
Learning Structured Prediction Models In Computer Vision
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Author : Fayao Liu
language : en
Publisher:
Release Date : 2015
Learning Structured Prediction Models In Computer Vision written by Fayao Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computer vision categories.
Most of the real world applications can be formulated as structured learning problems, in which the output domain can be arbitrary, e.g., a sequence or a graph. By modelling the structures (constraints and correlations) of the output variables, structured learning provides a more general learning scheme than simple binary classification or regression models. This thesis is dedicated to learning such structured prediction models, i.e., conditional random fields (CRFs) and their applications in computer vision. CRFs are popular probabilistic graphical models, which model the conditional distribution of the output variables given the observations. They play an essential role in the computer vision community and have found wide applications in various vision tasks-semantic labelling, object detection, pose estimation, to name a few. Specifically, we here focus on two challenging tasks in this thesis: image segmentation (also referred as semantic labelling) and depth estimation from single monocular images, which represent two types of CRFs models-discrete and continuous. In summary, we made three contributions in this thesis. First, we present a new approach to exploit tree potentials in CRFs for the task of image segmentation. This method combines the advantages of both CRFs and decision trees. Different from traditional methods, in which the potential functions of CRFs are defined as a linear combination of some pre-defined parametric models, we formulate the unary and the pairwise potentials as nonparametric forests-ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. We further show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary and multi-class segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials. Second, we propose to model the unary potentials of the CRFs using a convolutional neural network (CNN). The deep CNN is trained on the large-scale ImageNet dataset and transferred to image segmentation here for constructing unary potentials of super-pixels. The CRFs parameters are then learned within the max-margin framework using structured support vector machines (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labellings of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the potentials of the proposed method. Third, different from the previous two works, we address the problem of continuous CRFs learning, applied to the task of depth estimation from single images. Specifically, we formulate and learn the unary and pairwise potentials of a continuous CRFs model with CNN networks in a unified framework. We term this new method as deep convolutional neural fields, abbreviated as DCNF. It jointly explores the capacity of deep CNN and continuous CRFs. The proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. Specifically, in our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is ~ 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design very deep networks to pursue further performance gain. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method significantly outperforms state-of-the-art depth estimation approaches. We also show experimentally that the proposed method generalizes well to depth estimations of images unrelated to the training data. This indicates the potential of our method for benefiting other vision tasks.
Learning Structured Prediction Models
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Author : Ben Taskar
language : en
Publisher:
Release Date : 2004
Learning Structured Prediction Models written by Ben Taskar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Advanced Structured Prediction
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Author : Sebastian Nowozin
language : en
Publisher: MIT Press
Release Date : 2014-12-05
Advanced Structured Prediction written by Sebastian Nowozin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-05 with Computers categories.
An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný
Deep Learning And Computer Vision Models And Biomedical Applications
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Author : Uma N. Dulhare
language : en
Publisher: Springer Nature
Release Date : 2025-03-08
Deep Learning And Computer Vision Models And Biomedical Applications written by Uma N. Dulhare and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-08 with Computers categories.
This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Computer Vision Eccv 2010
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Author : Kostas Daniilidis
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-08-30
Computer Vision Eccv 2010 written by Kostas Daniilidis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-30 with Computers categories.
The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Computer Vision Eccv 2016
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Author : Bastian Leibe
language : en
Publisher: Springer
Release Date : 2016-09-16
Computer Vision Eccv 2016 written by Bastian Leibe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-16 with Computers categories.
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.
Probabilistic Graphical Models For Computer Vision
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Author : Qiang Ji
language : en
Publisher: Academic Press
Release Date : 2019-12-13
Probabilistic Graphical Models For Computer Vision written by Qiang Ji and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-13 with Technology & Engineering categories.
Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants.
Explanation Based Learning
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-30
Explanation Based Learning written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
What Is Explanation Based Learning A type of machine learning known as explanation-based learning, or EBL for short, takes advantage of an extremely robust, or even flawless, domain theory in order to generalize from training data or construct concepts. It also has a connection with encoding, or memory, which assists with learning. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Explanation-based learning Chapter 2: Computational linguistics Chapter 3: Natural language processing Chapter 4: Corpus linguistics Chapter 5: Parsing Chapter 6: Question answering Chapter 7: Link grammar Chapter 8: Grammar induction Chapter 9: Structured prediction Chapter 10: Deep linguistic processing (II) Answering the public top questions about explanation based learning. (III) Real world examples for the usage of explanation based learning in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of explanation based learning' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of explanation based learning.