Coverart for item
The Resource Bridging the semantic gap in image and video analysis, Halina Kwaśnicka, Lakhmi C. Jain, editors

Bridging the semantic gap in image and video analysis, Halina Kwaśnicka, Lakhmi C. Jain, editors

Label
Bridging the semantic gap in image and video analysis
Title
Bridging the semantic gap in image and video analysis
Statement of responsibility
Halina Kwaśnicka, Lakhmi C. Jain, editors
Contributor
Editor
Subject
Language
eng
Summary
This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on
Member of
Cataloging source
GW5XE
Dewey number
006
Illustrations
illustrations
Index
index present
LC call number
QA76.5913
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Kwaśnicka, Halina
  • Jain, L. C.
Series statement
Intelligent systems reference library,
Series volume
volume 145
http://library.link/vocab/subjectName
  • Semantic computing
  • Image analysis
  • Engineering
  • Computational Intelligence
  • Semantics
  • Artificial Intelligence (incl. Robotics)
  • Signal, Image and Speech Processing
  • Image Processing and Computer Vision
Label
Bridging the semantic gap in image and video analysis, Halina Kwaśnicka, Lakhmi C. Jain, editors
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; 1 Semantic Gap in Image and Video Analysis: An Introduction; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; References; 2 Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study; 2.1 Introduction; 2.2 Low-Level Feature Detectors and Descriptors; 2.2.1 SIFT, SURF, ORB, and A-KAZE Extractors; 2.2.2 PHOG, WGCH, and Haralick Extractors; 2.3 Low-Level Feature Comparison and Discussion; 2.3.1 Behavior and Robustness; 2.3.2 Matching Process; 2.4 Conclusions; References
  • 3 Scale-Insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images3.1 Introduction; 3.2 Summary of MSER and SIMSER Features; 3.2.1 MSER Features; 3.2.2 SIMSER Features; 3.2.3 Segmentation Using MSER Blobs; 3.3 SIMSER-Based Image Segmentation; 3.3.1 Image Smoothing in SIMSER Detection; 3.3.2 SIMSER Detection in Color Images; 3.4 Concluding Remarks; References; 4 Active Partitions in Localization of Semantically Important Image Structures; 4.1 Introduction; 4.2 Background; 4.2.1 Active Contours; 4.2.2 Knowledge; 4.3 Active Partitions; 4.3.1 Representation
  • 4.3.2 Partition4.3.3 Evolution; 4.4 Example; 4.4.1 Global Analysis; 4.4.2 Local Analysis; 4.5 Summary; References; 5 Model-Based 3D Object Recognition in RGB-D Images; 5.1 Introduction; 5.2 Knowledge Representation Hierarchy; 5.2.1 Related Work; 5.2.2 Proposed RGB-D Data Hierarchy; 5.3 3D Object Modelling; 5.3.1 Geometric Primitives; 5.3.2 Complex Objects; 5.4 System Framework; 5.4.1 Solution Principles; 5.4.2 Knowledge-Based Framework; 5.4.3 Semantic Net; 5.4.4 Bayesian Net; 5.4.5 The Basic Control; 5.5 System Implementation; 5.5.1 Model Structure; 5.5.2 Object Instances
  • 5.6 Testing Scenarios5.6.1 Data Acquisition; 5.6.2 Data Preprocessing and Extension; 5.6.3 Segmentation; 5.6.4 Hypothesis Generation; 5.6.5 Hypothesis Update and Verification; 5.6.6 Method Vulnerabilities; 5.7 Conclusions; References; 6 Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning; 6.1 The Limitations of Video Metadata and Feature Descriptors; 6.1.1 Core Video Metadata Standards; 6.1.2 Feature Extraction for Concept Mapping; 6.1.3 Machine Learning in Video Content Analysis; 6.1.4 The Semantic Gap
  • 6.2 Semantic Enrichment of Audiovisual Contents6.2.1 Video Semantics; 6.2.2 Spatiotemporal Video Annotation Using Formal Knowledge Representation; 6.2.3 Vocabularies and Ontologies; 6.2.4 Semantic Enrichment of Videos with Linked Data; 6.2.5 Spatiotemporal Annotation in Action; 6.3 Ontology-Based Video Scene Interpretation; 6.3.1 Video Event Recognition via Reasoning Over Temporal DL Axioms; 6.3.2 Video Event Recognition Using SWRL Rules; 6.3.3 Handling the Uncertainty of Concept Depiction with Fuzzy Axioms
Control code
SPR1025329337
Dimensions
unknown
Extent
1 online resource (x, 163 pages)
File format
unknown
Form of item
online
Isbn
9783319738918
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-73891-8
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1025329337
  • (OCoLC)1025329337
Label
Bridging the semantic gap in image and video analysis, Halina Kwaśnicka, Lakhmi C. Jain, editors
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; 1 Semantic Gap in Image and Video Analysis: An Introduction; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; References; 2 Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study; 2.1 Introduction; 2.2 Low-Level Feature Detectors and Descriptors; 2.2.1 SIFT, SURF, ORB, and A-KAZE Extractors; 2.2.2 PHOG, WGCH, and Haralick Extractors; 2.3 Low-Level Feature Comparison and Discussion; 2.3.1 Behavior and Robustness; 2.3.2 Matching Process; 2.4 Conclusions; References
  • 3 Scale-Insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images3.1 Introduction; 3.2 Summary of MSER and SIMSER Features; 3.2.1 MSER Features; 3.2.2 SIMSER Features; 3.2.3 Segmentation Using MSER Blobs; 3.3 SIMSER-Based Image Segmentation; 3.3.1 Image Smoothing in SIMSER Detection; 3.3.2 SIMSER Detection in Color Images; 3.4 Concluding Remarks; References; 4 Active Partitions in Localization of Semantically Important Image Structures; 4.1 Introduction; 4.2 Background; 4.2.1 Active Contours; 4.2.2 Knowledge; 4.3 Active Partitions; 4.3.1 Representation
  • 4.3.2 Partition4.3.3 Evolution; 4.4 Example; 4.4.1 Global Analysis; 4.4.2 Local Analysis; 4.5 Summary; References; 5 Model-Based 3D Object Recognition in RGB-D Images; 5.1 Introduction; 5.2 Knowledge Representation Hierarchy; 5.2.1 Related Work; 5.2.2 Proposed RGB-D Data Hierarchy; 5.3 3D Object Modelling; 5.3.1 Geometric Primitives; 5.3.2 Complex Objects; 5.4 System Framework; 5.4.1 Solution Principles; 5.4.2 Knowledge-Based Framework; 5.4.3 Semantic Net; 5.4.4 Bayesian Net; 5.4.5 The Basic Control; 5.5 System Implementation; 5.5.1 Model Structure; 5.5.2 Object Instances
  • 5.6 Testing Scenarios5.6.1 Data Acquisition; 5.6.2 Data Preprocessing and Extension; 5.6.3 Segmentation; 5.6.4 Hypothesis Generation; 5.6.5 Hypothesis Update and Verification; 5.6.6 Method Vulnerabilities; 5.7 Conclusions; References; 6 Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning; 6.1 The Limitations of Video Metadata and Feature Descriptors; 6.1.1 Core Video Metadata Standards; 6.1.2 Feature Extraction for Concept Mapping; 6.1.3 Machine Learning in Video Content Analysis; 6.1.4 The Semantic Gap
  • 6.2 Semantic Enrichment of Audiovisual Contents6.2.1 Video Semantics; 6.2.2 Spatiotemporal Video Annotation Using Formal Knowledge Representation; 6.2.3 Vocabularies and Ontologies; 6.2.4 Semantic Enrichment of Videos with Linked Data; 6.2.5 Spatiotemporal Annotation in Action; 6.3 Ontology-Based Video Scene Interpretation; 6.3.1 Video Event Recognition via Reasoning Over Temporal DL Axioms; 6.3.2 Video Event Recognition Using SWRL Rules; 6.3.3 Handling the Uncertainty of Concept Depiction with Fuzzy Axioms
Control code
SPR1025329337
Dimensions
unknown
Extent
1 online resource (x, 163 pages)
File format
unknown
Form of item
online
Isbn
9783319738918
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-73891-8
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1025329337
  • (OCoLC)1025329337

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