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Author: Hongyu Mou Publisher: ISBN: Category : Statistics Languages : en Pages : 0
Book Description
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-levelunderstanding from digital images or videos. The tasks of computer vision include methods for acquiring, processing analyzing and understanding digital images. In this dissertation, we present approaches for obtaining meaningful representations in computer vision. The second chapter proposes a new method for dictionary learning that uses Feature Selection with Annealing (FSA) to control the representation sparsity directly. This method obtains a smaller reconstruction error than LARS or IRLS and the sparse representation can be used for image classification, where it obtains a smaller misclassification error than LARS and IRLS. In the third chapter, we introduce a novel neuron formulation that can be used for obtaining a tight representation near the training examples and thus preventing high confidence predictions on examples that are far away from the training data. The proposed a neuron uses ReLU as the activation function and has compact support, which means that its output is zero outside a bounded domain. We also show how to avoid difficulties in training a neural network with such neurons. Furthermore, the experimental findings on standard benchmark datasets show that the proposed approach has smaller test errors than the state-of-the-art competing methods and outperforms the competing methods in detecting out-of-distribution samples on two out of three datasets. In the fourth chapter, we prove that a neural network with such compact support neurons has the universal approximation property. This means that the network can approximate any continuous and integrable function with an arbitrary degree of accuracy. In the last chapter, we use the feature of CSNN for few shot learning by recognizing data from a new class as out-of-distribution data and it shows good performance.
Author: Hongyu Mou Publisher: ISBN: Category : Statistics Languages : en Pages : 0
Book Description
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-levelunderstanding from digital images or videos. The tasks of computer vision include methods for acquiring, processing analyzing and understanding digital images. In this dissertation, we present approaches for obtaining meaningful representations in computer vision. The second chapter proposes a new method for dictionary learning that uses Feature Selection with Annealing (FSA) to control the representation sparsity directly. This method obtains a smaller reconstruction error than LARS or IRLS and the sparse representation can be used for image classification, where it obtains a smaller misclassification error than LARS and IRLS. In the third chapter, we introduce a novel neuron formulation that can be used for obtaining a tight representation near the training examples and thus preventing high confidence predictions on examples that are far away from the training data. The proposed a neuron uses ReLU as the activation function and has compact support, which means that its output is zero outside a bounded domain. We also show how to avoid difficulties in training a neural network with such neurons. Furthermore, the experimental findings on standard benchmark datasets show that the proposed approach has smaller test errors than the state-of-the-art competing methods and outperforms the competing methods in detecting out-of-distribution samples on two out of three datasets. In the fourth chapter, we prove that a neural network with such compact support neurons has the universal approximation property. This means that the network can approximate any continuous and integrable function with an arbitrary degree of accuracy. In the last chapter, we use the feature of CSNN for few shot learning by recognizing data from a new class as out-of-distribution data and it shows good performance.
Author: Qiang Zhang Publisher: Morgan & Claypool Publishers ISBN: 1627057781 Category : Technology & Engineering Languages : en Pages : 153
Book Description
The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.
Author: Bogdan Dumitrescu Publisher: Springer ISBN: 3319786741 Category : Technology & Engineering Languages : en Pages : 289
Book Description
This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
Author: Jose Luis Rojo-Alvarez Publisher: John Wiley & Sons ISBN: 1118611799 Category : Technology & Engineering Languages : en Pages : 665
Book Description
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
Author: Fengyu Cong Publisher: Springer ISBN: 3319590812 Category : Computers Languages : en Pages : 614
Book Description
This book constitutes the refereed proceedings of the 14th International Symposium on Neural Networks, ISNN 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017. The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions. The papers cover topics like perception, emotion and development, action and motor control, attractor and associative memory, neurodynamics, complex systems, and chaos.
Author: Publisher: ISBN: Category : Languages : en Pages : 10
Book Description
Sparse coding-that is, modelling data vectors as sparse linear combinations of basis elements-is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
Author: Ke-Lin Du Publisher: Springer Science & Business Media ISBN: 1447155718 Category : Technology & Engineering Languages : en Pages : 834
Book Description
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Author: Igor V. Tetko Publisher: Springer Nature ISBN: 3030304841 Category : Computers Languages : en Pages : 807
Book Description
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Author: Baiyang Liu Publisher: ISBN: Category : Computer vision Languages : en Pages : 79
Book Description
This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.
Author: Gholamreza Anbarjafari Publisher: BoD – Books on Demand ISBN: 178923316X Category : Computers Languages : en Pages : 186
Book Description
This book takes the vocal and visual modalities and human-robot interaction applications into account by considering three main aspects, namely, social and affective robotics, robot navigation, and risk event recognition. This book can be a very good starting point for the scientists who are about to start their research work in the field of human-robot interaction.
Author: Hongyu Mou
Author: Hongyu Mou
Author: Qiang Zhang
Author: Bogdan Dumitrescu
Author: Jose Luis Rojo-Alvarez
Author: Fengyu Cong
Author: 
Author: Ke-Lin Du
Author: Igor V. Tetko
Author: Baiyang Liu
Author: Gholamreza Anbarjafari