Development of Columbia's video on demand testbed

Chang, Shih-Fu; Eleftheriadis, Alexandros; Anastassiou, Dimitris

doi:10.1016/0923-5965(95)00047-x

Cited by 35 publications

(11 citation statements)

References 17 publications

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“…The QBIC approach is limited to using low-level features to achieve some degree of automation and domain independence. Following that, several systems such as VisualSeek (Chang et al, 1997;Smith & Chang, 1996), PicHunter (Cox, Miller, Minka, Papathomas, & Yianilos, 2000), Netra (Ma & Manjunath, 1999), and MARS (Mehrotra, Rui, Ortega-Binderberger, Huang, 1997), etc. ) have exploited the use of multidimensional indexing.…”

Section: Related Work In Image Retrievalmentioning

confidence: 98%

“…However, when the dimensionality of the feature vectors is large, the performance of these indexing techniques deteriorates. VisualSeek (Chang et al, 1997;Smith & Chang, 1996) used color as a discriminating feature for image retrieval. For a given query, the images in the image database are ranked based on the real distance between the query image and the images in the image database.…”

Section: Related Work In Image Retrievalmentioning

confidence: 99%

“…The characterization of an approach to CBIR is influenced by the image features to be extracted, the level of abstraction to be revealed in the features, and the extent of desired domain independence. The use of low-level (or measurable, automatically extractable) features (Chang, Eleftheriadis, & Anastassiou, 1997;Faloutsos, 1994;Flickner et al, 1995), for example, color, texture or shape, in image retrieval makes the approach automatic but not necessarily efficient. Inter-image distances computed using the low-level features are called "real" inter-image distances.…”

Section: Introductionmentioning

confidence: 99%

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A cluster‐based approach for efficient content‐based image retrieval using a similarity‐preserving space transformation method

Shah

Raghavan

Dhatric

et al. 2006

J. Am. Soc. Inf. Sci.

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The techniques of clustering and space transformation have been successfully used in the past to solve a number of pattern recognition problems. In this article, the authors propose a new approach to content-based image retrieval (CBIR) that uses (a) a newly proposed similarity-preserving space transformation method to transform the original low-level image space into a highlevel vector space that enables efficient query processing, and (b) a clustering scheme that further improves the efficiency of our retrieval system. This combination is unique and the resulting system provides synergistic advantages of using both clustering and space transformation. The proposed space transformation method is shown to preserve the order of the distances in the transformed feature space. This strategy makes this approach to retrieval generic as it can be applied to object types, other than images, and feature spaces more general than metric spaces. The CBIR approach uses the inexpensive "estimated" distance in the transformed space, as opposed to the computationally inefficient "real" distance in the original space, to retrieve the desired results for a given query image. The authors also provide a theoretical analysis of the complexity of their CBIR approach when used for color-based retrieval, which shows that it is computationally more efficient than other comparable approaches. An extensive set of experiments to test the efficiency and effectiveness of the proposed approach has been performed. The results show that the approach offers superior response time (improvement of 1-2 orders of magnitude compared to retrieval approaches that either use pruning techniques like indexing, clustering, etc., or space transformation, but not both) with sufficiently high retrieval accuracy.

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Section: Related Work In Image Retrievalmentioning

confidence: 98%

Section: Related Work In Image Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A cluster‐based approach for efficient content‐based image retrieval using a similarity‐preserving space transformation method

Shah

Raghavan

Dhatric

et al. 2006

J. Am. Soc. Inf. Sci.

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“…We then provide an example of this methodology by applying it to the Columbia VoD testbed [8]. The goal of our model is to provide an end-to-end QoS.…”

Section: Introductionmentioning

confidence: 99%

A practical methodology for guaranteeing quality of service for video-on-demand

Zamora

Jacobs²,

Eleftheriadis³

et al. 2000

IEEE Trans. Circuits Syst. Video Technol.

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A novel and simple approach for defining end-to-end quality of service (QoS) in video-on-demand (VoD) services is presented. Using this approach, we derive a schedulable region for a video server which guarantees end-to-end QoS, where a specific QoS required in the video client translates into a QoS specification for the video server. Our methodology is based on a generic model for VoD services, which is extendible to any VoD system. In this kind of system, both the network and the video server are potential sources of QoS degradation. Specifically, we examine the effect that impairments in the video server and video client have on the video quality perceived by the end user. The Columbia VoD testbed is presented as an example to validate the model through experimental results. Our model can be connected to network QoS admission-control models to create a unified approach for admission control of incoming video requests in the video server and network.

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“…In either case, simplifying assumptions are necessarily made (e.g., abstracting out control/signaling overhead, operating system issues such as the interaction between disk and CPU scheduling, multicast join/leave times, and more) to evaluate performance. While there are a number of existing production (e.g., Darwin, RealServer, Windows Media Server, Oracle Video Server) and experimental video server efforts [8][9][10][11][12][13][14][15][16][17][18], all of them use traditional unicast or multicast streaming.…”

Section: Introductionmentioning

confidence: 99%

Periodic broadcast and patching services - implementation, measurement and analysis in an internet streaming video testbed

et al. 2003

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Multimedia streaming applications can consume a significant amount of server and network resources. Periodic broadcast and patching are two approaches that use multicast transmission and client buffering in innovative ways to reduce server and network load, while at the same time allowing asynchronous access to multimedia streams by a large number of clients. Current research in this area has focussed primarily on the algorithmic aspects of these approaches, with evaluation performed via analysis or simulation. In this paper, we describe the design and implementation of a flexible streaming video server and client test bed that implements both periodic broadcast and patching, and explore the issues that arise when implementing these algorithms using laboratory and internet-based test beds. We present measurements detailing the overheads associated with the various server components (signaling, transmission schedule computation, data retrieval and transmission), the interactions between the various components of the architecture, and the overall end-to-end performance. We also discuss the importance of an appropriate server application-level caching policy for reducing the needed disk bandwidth at the server. We conclude with a discussion of the insights gained from our implementation and experimental evaluation.

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Development of Columbia's video on demand testbed

Cited by 35 publications

References 17 publications

A cluster‐based approach for efficient content‐based image retrieval using a similarity‐preserving space transformation method

A cluster‐based approach for efficient content‐based image retrieval using a similarity‐preserving space transformation method

A practical methodology for guaranteeing quality of service for video-on-demand

Periodic broadcast and patching services - implementation, measurement and analysis in an internet streaming video testbed

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