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Recent Advances in Intelligent Image Search and Video Retrieval -

Recent Advances in Intelligent Image Search and Video Retrieval

Chengjun Liu (Herausgeber)

Buch | Hardcover
XVII, 235 Seiten
2017 | 1st ed. 2017
Springer International Publishing (Verlag)
978-3-319-52080-3 (ISBN)
CHF 299,55 inkl. MwSt
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This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. 

Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring.

Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.

Feature Representation and Extraction for Image Search and Video Retrieval.- Learning and Recognition Methods for Image Search and Video Retrieval.-  Improved Soft Assignment Coding for Image Classification.- Inheritable Color Space (InCS) and Generalized InCS Framework with Applications to Kinship Verification.- Novel Sparse Kernel Manifold Learner for Image Classification Applications.- A New Efficient SVM (eSVM) with Applications to Accurate and Efficient Eye Search in Images.- SIFT Features in Multiple Color Spaces for Improved Image Classification.- Clothing Analysis for Subject Identification and Retrieval.- Performance Evaluation of Video Analytics for Traffic Incident Detection and Vehicle Counts Collection.

Erscheinungsdatum
Reihe/Serie Intelligent Systems Reference Library
Zusatzinfo XVII, 235 p. 88 illus., 85 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik
Schlagworte Artificial Intelligence • artificial intelligence (incl. robotics) • Computational Intelligence • computer vision • Engineering • Engineering: general • Image Processing • image processing and computer vision • Image Search, Video Retrieval • Intelligent Techniques • internet of things • Local Binary Pattern (LBP) • Pyramid Histogram of Oriented Gradients (PHOG) • Robotics • Scale-Invariant Feature Transform (SIFT) • Smart City • Spatial Pyramid Matching (SPM) • Support Vector Machine (SVM)
ISBN-10 3-319-52080-6 / 3319520806
ISBN-13 978-3-319-52080-3 / 9783319520803
Zustand Neuware
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