ENGLISH

Bridging the Semantic Gap in Image and Video Analysis

Book information

Publisher
Springer International Publishing
Year
2018
ISBN
978-3-319-73890-1, 978-3-319-73891-8
Language
english
Format
PDF
Filesize
6 MB (6344903 bytes)
Series
Intelligent Systems Reference Library 145
Edition
1
Pages
X, 163\171
Time added
2018-03-04 00:00:30

Description

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. Front Matter ....Pages i-x Semantic Gap in Image and Video Analysis: An Introduction (Halina Kwaśnicka, Lakhmi C. Jain)....Pages 1-6 Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study (D. Avola, L. Cinque, G. L. Foresti, N. Martinel, D. Pannone, C. Piciarelli)....Pages 7-29 Scale-Insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images (Andrzej Śluzek)....Pages 31-50 Active Partitions in Localization of Semantically Important Image Structures (Arkadiusz Tomczyk)....Pages 51-72 Model-Based 3D Object Recognition in RGB-D Images (Maciej Stefańczyk, Włodzimierz Kasprzak)....Pages 73-96 Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning (Leslie F. Sikos)....Pages 97-122 Deep Learning—A New Era in Bridging the Semantic Gap (Urszula Markowska-Kaczmar, Halina Kwaśnicka)....Pages 123-159 Back Matter ....Pages 161-163

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