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Kwasnicka, Halina.

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Bridging the semantic gap in image and video analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bridging the semantic gap in image and video analysis/ edited by Halina Kwasnicka, Lakhmi C. Jain.
other author:	Kwasnicka, Halina.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	x, 163 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning - a New Era in Bridging the Semantic Gap.
Contained By:	Springer eBooks
Subject:	Semantic computing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-73891-8
ISBN:	9783319738918

Bridging the semantic gap in image and video analysis
Bridging the semantic gap in image and video analysis[electronic resource] /edited by Halina Kwasnicka, Lakhmi C. Jain. - Cham :Springer International Publishing :2018. - x, 163 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v.1451868-4394 ;. - Intelligent systems reference library ;v.145..

Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning - a New Era in Bridging the Semantic Gap.

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.

ISBN: 9783319738918

Standard No.: 10.1007/978-3-319-73891-8doiSubjects--Topical Terms:

1067496
Semantic computing.

LC Class. No.: QA76.5913

Dewey Class. No.: 006

Bridging the semantic gap in image and video analysis
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245 0 0 $a Bridging the semantic gap in image and video analysis $h [electronic resource] / $c edited by Halina Kwasnicka, Lakhmi C. Jain.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a x, 163 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.145
505 0 $a Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning - a New Era in Bridging the Semantic Gap.
520 $a 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.
650 0 $a Semantic computing. $3 1067496
650 0 $a Image analysis. $3 561018
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Semantics. $3 520060
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Image Processing and Computer Vision. $3 891070
700 1 $a Kwasnicka, Halina. $3 1532799
700 1 $a Jain, Lakhmi C. $3 893365
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.145. $3 3301968
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-73891-8
950 $a Engineering (Springer-11647)