Multimedia Data Storage

2018 ◽  
pp. 2383-2386
Author(s):  
Jeffrey Xu Yu
Keyword(s):  
Data Storage ◽  
Multimedia Data

Efficient Share Generator for Slicing and Securely Retrieving the Cloud-Hosted Heterogeneous Multimedia Data

2021 ◽  
pp. 20-32
Author(s):  
admin admin ◽  
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Storage ◽  
Implementation Strategies ◽  
Critical Issue ◽  
Multimedia Data ◽  
Storage And Retrieval ◽  
Remote Storage ◽  
Source Transformation ◽  
Cloud Servers

Recently, the security of heterogeneous multimedia data becomes a very critical issue, substantially with the proliferation of multimedia data and applications. Cloud computing is the hidden back-end for storing heterogeneous multimedia data. Notwithstanding that using cloud storage is indispensable, but the remote storage servers are untrusted. Therefore, one of the most critical challenges is securing multimedia data storage and retrieval from the untrusted cloud servers. This paper applies a Shamir Secrete-Sharing scheme and integrates with cloud computing to guarantee efficiency and security for sensitive multimedia data storage and retrieval. The proposed scheme can fully support the comprehensive and multilevel security control requirements for the cloud-hosted multimedia data and applications. In addition, our scheme is also based on a source transformation that provides powerful mutual interdependence in its encrypted representation—the Share Generator slices and encrypts the multimedia data before sending it to the cloud storage. The extensive experimental evaluation on various configurations confirmed the effectiveness and efficiency of our scheme, which showed excellent performance and compatibility with several implementation strategies.

Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints

2011 ◽  
pp. 145-170 ◽  
Author(s):  
Chotirat “Ann” Ratanamahatana ◽  
Eamonn Keogh ◽  
Vit Niennattrakul
Keyword(s):  
Time Series ◽  
Image Retrieval ◽  
Data Storage ◽  
Distance Measure ◽  
Series Representation ◽  
Feedback System ◽  
Multimedia Data ◽  
Video Frames ◽  
Computationally Intensive ◽  
Dynamic Time

After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically increased in the database and data mining community. This includes videos, images, and handwriting, where we now have higher expectations in exploiting these data at hand. We argue however, that much of this work’s narrow focus on efficiency and scalability has come at the cost of usability and effectiveness. Typical manipulations are in some forms of video/image processing, which require fairly large amounts for storage and are computationally intensive. In this work, we will demonstrate how these multimedia data can be reduced to a more compact form, that is, time series representation, while preserving the features of interest, and can then be efficiently exploited in Content-Based Image Retrieval. We also introduce a general framework that learns a distance measure with arbitrary constraints on the warping path of the Dynamic Time Warping calculation. We demonstrate utilities of our approach on both classification and query retrieval tasks for time series and other types of multimedia data including images, video frames, and handwriting archives. In addition, we show that incorporating this framework into the relevance feedback system, a query refinement can be used to further improve the precision/recall by a wide margin.

PDSRS: An attribute based approach for multimedia data storage and retrieval

2011 ◽  
pp. 119-122
Author(s):  
Sonali Ajankar ◽  
Sanjay Nalbalwar ◽  
Z. A. Usmani
Keyword(s):  
Data Storage ◽  
Multimedia Data ◽  
Storage And Retrieval

Towards a framework for large-scale multimedia data storage and processing on Hadoop platform

2013 ◽  
Vol 68 (1) ◽  
pp. 488-507 ◽  
Author(s):  
Wei Kuang Lai ◽  
Yi-Uan Chen ◽  
Tin-Yu Wu ◽  
Mohammad S. Obaidat
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Multimedia Data ◽  
Hadoop Platform

Wide-area-distributed disaster-tolerant file system for multimedia data storage

1997 ◽  
Author(s):  
Setsuko Murata ◽  
Shigetaro Iwatsu ◽  
Masahiro Ueno ◽  
Nobuyoshi Izawa ◽  
Katsunori Ishii
Keyword(s):  
Data Storage ◽  
File System ◽  
Multimedia Data ◽  
Wide Area

Multimedia Data Mining Concept

2008 ◽  
pp. 3611-3620
Author(s):  
Janusz Swierzowicz
Keyword(s):  
Digital Media ◽  
Data Storage ◽  
Multimedia Data ◽  
Digital Data ◽  
Data Sets ◽  
Still Images ◽  
Three Dimensional Objects ◽  
Distributed Query ◽  
High Voice ◽  
The 1960S

The development of information technology is particularly noticeable in the methods and techniques of data acquisition, high-performance computing, and bandwidth frequency. According to a newly observed phenomenon, called a storage low (Fayyad & Uthurusamy, 2002), the capacity of digital data storage is doubled every 9 months with respect to the price. Data can be stored in many forms of digital media, for example, still images taken by a digital camera, MP3 songs, or MPEG videos from desktops, cell phones, or video cameras. Such data exceeds the total cumulative handwriting and printing during all of recorded human history (Fayyad, 2001). According to current analysis carried out by IBM Almaden Research (Swierzowicz, 2002), data volumes are growing at different speeds. The fastest one is Internet-resource growth: It will achieve the digital online threshold of exabytes within a few years (Liautaud, 2001). In these fast-growing volumes of data environments, restrictions are connected with a human’s low data-complexity and dimensionality analysis. Investigations on combining different media data, multimedia, into one application have begun as early as the 1960s, when text and images were combined in a document. During the research and development process, audio, video, and animation were synchronized using a time line to specify when they should be played (Rowe & Jain, 2004). Since the middle 1990s, the problems of multimedia data capture, storage, transmission, and presentation have extensively been investigated. Over the past few years, research on multimedia standards (e.g., MPEG-4, X3D, MPEG-7) has continued to grow. These standards are adapted to represent very complex multimedia data sets; can transparently handle sound, images, videos, and 3-D (three-dimensional) objects combined with events, synchronization, and scripting languages; and can describe the content of any multimedia object. Different algorithms need to be used in multimedia distribution and multimedia database applications. An example is an image database that stores pictures of birds and a sound database that stores recordings of birds (Kossmann, 2000). The distributed query that asks for “top ten different kinds of birds that have black feathers and a high voice” is described there by Kossmann (2000, p.436).

Multimodal Analysis in Multimedia Using Symbolic Kernels

2011 ◽  
pp. 842-847
Author(s):  
Hrishikesh B. Aradhye ◽  
Chitra Dorai
Keyword(s):  
Data Storage ◽  
Video Clip ◽  
Multimedia Data ◽  
Communications Technology ◽  
Multimodal Analysis ◽  
Automated Content Analysis ◽  
Indexing And Retrieval ◽  
The Face ◽  
Broadcast Programs ◽  
Search And Retrieval

The rapid adoption of broadband communications technology, coupled with ever-increasing capacity-to-price ratios for data storage, has made multimedia information increasingly more pervasive and accessible for consumers. As a result, the sheer volume of multimedia data available has exploded on the Internet in the past decade in the form of Web casts, broadcast programs, and streaming audio and video. However, indexing, search, and retrieval of this multimedia data is still dependent on manual, text-based tagging (e.g., in the form of a file name of a video clip). However, manual tagging of media content is often bedeviled by an inadequate choice of keywords, incomplete and inconsistent terms used, and the subjective biases of the annotator introduced in his or her descriptions of content adversely affecting accuracy in the search and retrieval phase. Moreover, manual annotation is extremely time-consuming, expensive, and unscalable in the face of ever-growing digital video collections. Therefore, as multimedia get richer in content, become more complex in format and resolution, and grow in volume, the urgency of developing automated content analysis tools for indexing and retrieval of multimedia becomes easily apparent.

Revisiting FPGA Implementation of Digital Filters and Exploring Approximate Computing on Biomedical Signals

2020 ◽  
Vol 10 (9) ◽  
pp. 2000-2004
Author(s):  
Wang Hui ◽  
Gong Chang ◽  
S. Saravanan ◽  
V. Gomathi ◽  
R. Valarmathi ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Noise Removal ◽  
Multimedia Data ◽  
Digital Data ◽  
Functional Verification ◽  
Approximate Computing ◽  
Human Beings ◽  
Volume Data ◽  
Simulation Results

In recent years, the approximate computing becomes popular in the era of VLSI (very large scale integration) domain to arrive better power, area, and delay outcomes at the cost of lower precision loss. Also, the human beings are not so intelligent to see/observe/listen the processed digital data; means even if some of the data loss occurs human beings are unable to notice them. This behavior set the engineers to research on approximate computing which are very useful in the multimedia data processing, data communications, high-volume data storage, etc. In this study, the experiments such as hum-noise removal, filters on QRS detection are implemented on an Altera FPGA EP4CEF29C7 device using Quartus II 13.1 synthesis software tool and the simulation results on device utilization reports, the speed and the power are obtained. Simulation results reveal that the approximate computational filters offer better power, area, and speed results than the conventional ones. Also, Matlab 9.4 (R2018a) simulation was used to carry out the functional verification of the actual and approximate filters.

Multimedia Data Storage

2009 ◽  
pp. 1814-1817
Author(s):  
Jeffrey Xu Yu
Keyword(s):  
Data Storage ◽  
Multimedia Data
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