Bridging The Semantic Gap In Image And Video Analysis
DOWNLOAD
Download Bridging The Semantic Gap In Image And Video Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Bridging The Semantic Gap In Image And Video Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Bridging The Semantic Gap In Image And Video Analysis
DOWNLOAD
Author : Halina Kwaśnicka
language : en
Publisher: Springer
Release Date : 2018-02-20
Bridging The Semantic Gap In Image And Video Analysis written by Halina Kwaśnicka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-20 with Technology & Engineering categories.
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.
Knowledge Driven Multimedia Information Extraction And Ontology Evolution
DOWNLOAD
Author : Georgios Paliouras
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-19
Knowledge Driven Multimedia Information Extraction And Ontology Evolution written by Georgios Paliouras and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-19 with Computers categories.
This book presents the state of the art in the areas of ontology evolution and knowledge-driven multimedia information extraction, placing an emphasis on how the two can be combined to bridge the semantic gap. This was also the goal of the EC-sponsored BOEMIE (Bootstrapping Ontology Evolution with Multimedia Information Extraction) project, to which the authors of this book have all contributed. The book addresses researchers and practitioners in the field of computer science and more specifically in knowledge representation and management, ontology evolution, and information extraction from multimedia data. It may also constitute an excellent guide to students attending courses within a computer science study program, addressing information processing and extraction from any type of media (text, images, and video). Among other things, the book gives concrete examples of how several of the methods discussed can be applied to athletics (track and field) events.
Exploration Of Visual Data
DOWNLOAD
Author : Sean Xiang Zhou
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Exploration Of Visual Data written by Sean Xiang Zhou and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines. The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data. Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Semantic Multimedia Modelling Interpretation For Annotation
DOWNLOAD
Author : Irfan Ullah
language : en
Publisher:
Release Date : 2011
Semantic Multimedia Modelling Interpretation For Annotation written by Irfan Ullah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user's high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems.
Concept Based Video Retrieval
DOWNLOAD
Author : Cees G. M. Snoek
language : en
Publisher: Now Publishers Inc
Release Date : 2009
Concept Based Video Retrieval written by Cees G. M. Snoek and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Database management categories.
In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human-computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.
Reduction Of Semantic Gap In Content Based Image Retrieval
DOWNLOAD
Author : Gita Das
language : en
Publisher:
Release Date : 2007
Reduction Of Semantic Gap In Content Based Image Retrieval written by Gita Das and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Database management categories.
Understanding Video Retrieval
DOWNLOAD
Author : Frank Hopfgartner
language : en
Publisher: VDM Publishing
Release Date : 2007
Understanding Video Retrieval written by Frank Hopfgartner and has been published by VDM Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Digital video categories.
With the improving capabilities of current hardware systems, there are ever growing possibilities to store and manipulate videos in a digital format, leading to a growing number of video archives. People build their own digital libraries from materials created through digital cameras and camcorders, and use systems such as YouTube to place this material on the web. Unfortunately, this data creation prowess is not matched by any comparable tools to organise and retrieve video information. There is a need to create new retrieval engines to assist the users in searching and finding video scenes they would like to see from many different video files. Unlike text retrieval systems, retrieval on digital video datasets is facing a serious problem: The Semantic Gap. This is the difference between low-level data representation of videos and the higher level concepts user associates with video. This book introduces several approaches to bridge this semantic gap, explains different evaluation strategies and presents state-of-the-art video retrieval tools. The target audience is everyone who is interested in getting to know the research approaches that led to the popular video retrieval tools.
Semantic Image Understanding
DOWNLOAD
Author : Jia Li
language : en
Publisher:
Release Date : 2011
Semantic Image Understanding written by Jia Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Human can effortlessly perceive rich amount of semantic information from our visual world including objects within it, the scene environment, and event/activity taking place etc. Such information has been critical for us to enjoy our life. In computer vision, an important, open problem is to endow computers/intelligent agents the ability to extract semantically meaningful information as human does. The primary goal of my research is to design and demonstrate visual recognition algorithms to bridge the gap between visual intelligence and human perception. Towards this goal, we have developed rigid statistical models to represent the large scale real-world challenging data especially those from Internet. Visual features are the starting-point of computer vision algorithms. We propose a novel high-level image representation to encode the abundant semantic and structural information within an image. We first focus on introducing principle generative models for modeling our rich visual world, from recognizing objects in an image, to a detailed understanding of scene/activity images, to inferring the relationship among large scale user images and related textual data. We propose a non-parametric topic model, hierarchical Dirichlet Process (HDP), in a robust noise rejection system for object recognition, learning the object model and re-ranking noisy web images containing the objects in an iterative online fashion. It learns the object model in a fully automatic way, freeing the researchers from heavy human labor in labeling training examples for recognizing objects. This framework has been tested on a large scale corpus of over 400 thousand images and also won the Software Robot first Prize in the 2007 Semantic Visual Recognition Competition. Understanding our visual world is beyond simply recognizing objects. We then present a generative model for understanding complex scenes that involve objects, humans and scene backgrounds to interact together. For detailed understanding of an image, we propose the very first model for event recognition in a static image by combining the objects appear in the event and the scene environment, where the event takes place. We are not only interested in the category prediction of an unknown image, but also in how pixels form coherent objects and the semantic concepts related to them. We propose the first principled graphical model that tackles three very challenging vision tasks in one framework: image classification, object annotation, and object segmentation. Our statistical model encodes the relationships of pixel visual properties, object identities, textual concepts and the image class. It is a much larger scale departure from the previous work, using real-world challenging user photos such as noisy, Flickr images and user tags to learn the model in an automatic framework. Interpreting single images is an important corner stone for inferring relationships among large scale images to effectively organize them. We propose a joint visual-textual model based upon the nested Chinese Restaurant Process (nCRP) model. Our model combines textual semantics (user tags) with image visual contents, which learns a semantically and visually meaningful image hierarchy on thousands of Flickr user images with noisy user tags. The hierarchy performs significantly better on image classification and annotation performance as a knowledge base comparing to the state-of-the-art algorithms. Visual recognition algorithms start from representation of the images, the socalled image feature. While the goal of visual recognition is to recognize object and scene contents that are semantically meaningful, all previous work have relied on lowlevel feature representations such as filter banks, textures, and colors, creating the well known semantic gap. We propose a fundamentally new image feature, Object Bank, which uses hundreds and thousands of object sensing filters (i.e. pre-trained object detectors) to represent an image. Instead of representing an image based on its color, texture or likewise, Object Bank depicts an image by objects appearing in the image and their locations. Encoding rich descriptive semantic and structural information of an image, Object Bank is extremely robust and powerful for complex scene understanding, including classification, retrieval and annotation.
Image Understanding By Socializing The Semantic Gap
DOWNLOAD
Author : Tiberio Uricchio
language : en
Publisher:
Release Date : 2016
Image Understanding By Socializing The Semantic Gap written by Tiberio Uricchio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Modeling Social And Temporal Context For Video Analysis
DOWNLOAD
Author : Zhen Qin
language : en
Publisher:
Release Date : 2015
Modeling Social And Temporal Context For Video Analysis written by Zhen Qin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Automatic tracking categories.
The ubiquity of videos requires effective content extraction tools to enable practical applications automatically. Computer vision research focuses on bridging the gap between raw data (pixel values) and video semantics, but information based only on image values are not sufficient, due to the visual ambiguities caused by varied camera characteristics, frequent occlusions, low resolution, large intra-class and small inter-class variation among object/activity/event classes, etc.