Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Application Software, Computer Graphics, General Computing
Cover of the book Sparse Representation, Modeling and Learning in Visual Recognition by Hong Cheng, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Hong Cheng ISBN: 9781447167143
Publisher: Springer London Publication: May 25, 2015
Imprint: Springer Language: English
Author: Hong Cheng
ISBN: 9781447167143
Publisher: Springer London
Publication: May 25, 2015
Imprint: Springer
Language: English

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

More books from Springer London

Cover of the book Electrocatalysis in Fuel Cells by Hong Cheng
Cover of the book Reinventing Ourselves: Contemporary Concepts of Identity in Virtual Worlds by Hong Cheng
Cover of the book Training in Minimal Access Surgery by Hong Cheng
Cover of the book Moving Targets by Hong Cheng
Cover of the book System Identification, Environmental Modelling, and Control System Design by Hong Cheng
Cover of the book Guide to Cloud Computing by Hong Cheng
Cover of the book Problem Based Urology by Hong Cheng
Cover of the book Tumours in Urology by Hong Cheng
Cover of the book Practical Signal and Image Processing in Clinical Cardiology by Hong Cheng
Cover of the book Case Studies in Abdominal and Pelvic Imaging by Hong Cheng
Cover of the book Emerging Concepts in Neuro-Oncology by Hong Cheng
Cover of the book Visual Analysis of Behaviour by Hong Cheng
Cover of the book Imaging and Technology in Urology by Hong Cheng
Cover of the book Congestion Control in Data Transmission Networks by Hong Cheng
Cover of the book Stroke Genetics by Hong Cheng
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy