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2000 ACM Workshops on multimedia, multimedia 2000

作者： Ioannou, S. Moschovitis, G. Ntalianis, K. Karpouzis, K. Kollias, S. Image Video and Multimedia Laboratory Electrical and Computer Engineering Department National Technical University of Athens Heroon Polytechniou 9 Athens157 80 Greece

ISBN: (纸本)9781581133110

Current multimedia databases contain a wealth of information in the form of audiovisual, as well as text data. Even though efficient search algorithms have been developed for either media, there still exists the need for abstract presentation and summarization of the results of database users' queries. Moreover, multimedia retrieval systems should be capable of providing the user with additional information related to the specific subject of the query, as well as suggest other topics which users with a similar profile are interested in. In this paper, we present a number of solutions to these issues, giving as an example an integrated architecture we have developed, along with notions that support efficient and secure Internet access and easy addition of new material. Segmentation of the video in shots is followed by shot classification in a number of predetermined categories. Generation of users' profiles according to the same categories, enhanced by relevance feedback, permits an efficient presentation of the retrieved video shots or characteristic frames in terms of the user interest in them. Moreover, this clustering scheme assists the notion of "lateral" links that enable the user to continue retrieval with data of similar nature or content to those already returned. Furthermore, user groups are formed and modeled by registering actual preferences and practices;this enables the system to "predict" information that is possibly relevant to specific users and present it along with the returned results. The concepts utilized in this system can be smoothly integrated in MPEG-7 compatible multimedia database systems. Copyright © ACM 2000.

关键词： Abstracting

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Feature representation and compression for content-based retrieval

Feature representation and compression for content-based ret...

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Conference on Visual Communications and Image Processing 2001

作者： Xie, H Ortega, A Univ So Calif Dept Elect Engn Syst Integrated Media Syst Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819439886

In semantic content-based image/video browsing and navigation systems, efficient mechanisms to represent and manage a large collection of digital images/videos are needed. Traditional keyword-based indexing describes the content of multimedia data through annotations such as text or keywords extracted manually by the user from a controlled vocabulary This textual indexing technique lacks the flexibility of satisfying various kinds of queries requested by database users and also requires huge amount of work for updating the information. Current content-based retrieval systems often extract a set of features such as color, texture, shape motion, speed, and position from the raw multimedia data automatically and store them as content descriptors. This content-based metadata differs from text-based metadata in that it supports wider varieties of queries and can be extracted automatically, thus providing a promising approach for efficient database access and management. When the raw data volume grows very large, explicitly extracting the content-information and storing it as metadata along with the images will improve querying performance since metadata requires much less storage than the raw image data and thus will be easier to manipulate. In this paper we maintain that storing metadata together with images will enable effective information management and efficient remote query. We also show, using a texture classification example, that this side information can be compressed while guaranteeing that the desired query accuracy is satisfied. We argue that the compact representation of the image contents not only reduces significantly the storage and transmission rate requirement, but also facilitates certain types of queries. algorithms are developed for optimized compression of this texture feature metadata given that tile goal is to maximize tile classification performance for a given rate budget.

关键词： metadata content-based retrieval texture classification compression bit allocation

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HIT-DML: A novel Digital Music Library

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6th International Conference on Asian Digital Libraries

作者： Wang, CK Li, JZ Shi, SF Harbin Inst Technol Dept Comp Sci & Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)3540206086

The design and implementation of Harbin Institute of Technology-Digital Music Library (HIT-DML) is presented in this paper. HIT-DML adopts a novel framework which is inherently based on database systems. In this framework, musical data is structurally stored in the database, some algorithms of musical computation are implemented as algebraic mirco-operations in the database management system, and thus database technologies and multimedia technologies are combined seamlessly. A musical feature-matching algorithm and the appropriate dynamic index are also applied in HIT-DML. HIT-DML can retrieve musical information based on content, especially against different kinds of musical instruments.

关键词： Digital Music Library content-based music information retrieval

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multimedia enriched ontologies for video digital libraries

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED systems 2007年第6期22卷 407-416页

作者： Bertini, Marco Del Bimbo, Alberto Torniai, Carlo Univ Firenze Florence Italy

The development of appropriate tools and solutions to support effective access to video content is one of the main challenges for video digital libraries. Different techniques for manual and automatic annotation and retrieval have been proposed in recent years. It is a common practice to use linguistic ontologies for video annotation and retrieval: video elements are classified by establishing relationships between video contents and linguistic terms that identify domain concepts at different abstraction levels. However, although linguistic terms are appropriate to distinguish event and object categories, they are inadequate when they must describe specific or complex patterns of events or video entities. Instead, in these cases, pattern specifications can be better expressed using visual prototypes, either images or video clips, that capture the essence of the event or entity. High level concepts, expressed trough linguistic terms, and patterns specification, represented by visual prototypes, can be both organized into new extended ontologies where images or video clips are added to the ontologies as specification of linguistic terms. This paper presents algorithms and techniques that employ enriched ontologies for video annotation and retrieval, and discusses a solution for their implementation for the soccer video domain. An unsupervised clustering method is proposed in order to create multimedia enriched ontologies by defining visual prototypes that represent specific patterns of highlights and adding them as visual concepts to the ontology. An algorithm that uses multimedia enriched ontologies to perform automatic soccer video annotation is proposed and results for typical highlights are presented. Annotation is performed associating occurrences of events, or entities, to higher level concepts by checking their similarity to visual concepts that are hierarchically linked to higher level semantics, using a dynamic programming approach. Usage of reasoning on the

关键词： Video Algorithm Annotation Ontology

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Toward Wi-Fi AP-Assisted content Prefetching for an On-Demand TV Series: A Learning-Based Approach

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2018年第7期28卷 1665-1676页

作者： Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a largescale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.

关键词： Wi-Fi AP content prefetching learning-based approach

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A Comprehensive Study on Various Encryption Techniques for Digital Images in multimedia Networks 2

A Comprehensive Study on Various Encryption Techniques for D...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Santhosha, Benaka Sudheesh, K.V. Kiran, K. Parameshachari, B.D. Dayanand Sagar University Harohalli Bengaluru Visvesveraya Technological University Department of CSE Belagavi India Vidyavardhaka College of Enginerring Mysuru Visvesveraya Technological University Department of ECE Belagavi India Nitte Meenakshi Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798350317558

Image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encryption, comparing and contrasting different encryption techniques and their benefits and limitations as well as real-world uses. In order to strengthen image data security against unwanted access and manipulation, we first offer some basic understanding of image encryption. The suitability of symmetric, asymmetric, and hybrid encryption techniques in various contexts is examined. We also explore the assessment criteria used to evaluate encryption algorithms, highlighting the significance of using suitable measures to precisely gauge security and effectiveness. We also discuss common issues with image encryption, like complicated key management and computational complexity. The review also explores future directions that picture encryption might take, including multi-media encryption techniques, resistance to cryptanalysis, and quantum image encryption. We also highlight how important image encryption is in a variety of fields, such as finance, healthcare, journalism, intellectual property protection, and military operations. We will conduct a thorough analysis of current cryptography schemes and multimedia encryption algorithms to provide a comprehensive overview of the current security landscape tailored to digital multimedia technology. The findings from this survey will enhance our understanding of the efficacy and dependability of secure multimedia encryption schemes, ultimately aiding in the development of more efficient and robust encryption methods for the future. This article aims to summarize and assess numerous algorithms using different methodologies based on multiple characteristics such as MSE, PSNR, NC, BER, and so on. © 2024 IEEE.

关键词： Cryptography Image multimedia Networks Security Attacks Steganography Watermarking

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content-based access of VRML libraries

Content-based access of VRML libraries

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IAPR International Workshop on multimedia Information Analysis and Retrieval, MINAR 1998

作者： Paquet, Eric Rioux, Marc Visual Information Technology Institute for Information Technology National Research Council Building M-50 Montreal Road OttawaONKlA 0R6 Canada

ISBN: (纸本)3540648267

This paper presents a new approach for the classification and retrieval of three-dimensional images and models from databases. A set of retrieval algorithms is introduced. These algorithms are content-based, meaning that the input is not made out of keywords but of three-dimensional models. Tensor of inertia, distribution of normals, distribution of cords and multiresolution analysis are used to describe each model. The database can be searched by scale, shape or color or any combination of these parameters. A user friendly interface makes the retrieval operation simple and intuitive and make it possible to edit reference models according to the specifications of the user. Experimental results using a database of more than 1000 VRML models are presented. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Database systems

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Proceedings of the 9th Workshop on multimedia Metadata, WMM 2009 - Held in Conjunction with the 13th French multimedia Conference on Compression and Representation of Audiovisual Signals, CORESA 2009

Proceedings of the 9th Workshop on Multimedia Metadata, WMM ...

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9th Workshop on multimedia Metadata, WMM 2009 - Held in Conjunction with the 13th French multimedia Conference on Compression and Representation of Audiovisual Signals, CORESA 2009

The proceedings contain 13 papers. The topics discussed include: case studies on context-aware mobile multimedia services;content-based image retrieval systems - reviewing and benchmarking;delivery context descriptions - a comparison and mapping model;a novel tool for quick video summarization using keyframe extraction techniques;authoring interactive mobile services using MPEG-7 and MPEG-4 LASeR;an user study on rich media mobile guide applications;what algorithms for urban routing on mobile devices?;towards the use of multimedia contents to represent events in vehicular ad hoc networks;a multimedia service with MPEG-7 metadata and context semantics;a standards-based generic approach for complex multimedia management;contribution to the modeling of multimedia metadata in a distributed architecture;media center oriented Linux operating system;and a multi-level access control scheme for multimedia database.

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SIGMAP 2007 - Proceedings of the International Conference on Signal Processing and multimedia Applications

SIGMAP 2007 - Proceedings of the International Conference on...

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SIGMAP 2007 - International Conference on Signal Processing and multimedia Applications

The proceedings contain 74 papers. The topics discussed include: IMS secured content delivery over peer-to-peer networks;a methodology for the development of live audio and video services;performance of audio/video services on constrained variable user access lines;fusion predictors for discrete-time linear systems with multisensor environment;small tricks to enhance the accuracy of license plate character recognition;unsupervised algorithms for segmentation and clustering applied to soccer players classification;an improved super resolution reconstruction algorithm for video sequence;searching for a robust MFCC-based parameterization for ASR application;image restoration - a new explicit approach in filtering and restoration of digital images;adaptive and cooperative segmentation system for mono- and multi-component images;and a three-layer system for image retrieval.

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Efficient Processing and Delivery of multimedia Data

Efficient Processing and Delivery of Multimedia Data

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作者： Tang, Linpeng Princeton University

学位级别：Ph.D.

The explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by tailoring software design to hardware properties, as well as optimize systems by exploiting workload characteristics using learning-based approaches. First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that perform poorly on flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low over- head. By providing a priority queue interface, it allows a variety of caching algorithms to be easily implemented. Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it is infeasible to encode all videos uploaded to Face- book with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos, and the challenge lies in how to accurately and scalably run popularity prediction to detect those videos before- hand. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy. Lastly, this thesis investigates how to accelerate deep learning models on many-core CPUs. D

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