Parallel CBIR implementations with load balancing algorithms

Bosque, José Luis; Robles, Oscar D.; Pastor, Luis; Rodríguez, Antonio

doi:10.1016/j.jpdc.2006.04.014

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…Nevertheless, the response times increased from 12 to 43 s, which means a factor of 4. This is mostly due to the fact that the communication overhead is almost constant and thus it has a higher impact when the workload is small (especially the workload is very small when the FC is run in the developed parallel version 51 in a compound element, like a cluster). However, from these experiments it can be stated that performance achieved is satisfactory and meets the goals presented previously, offering good level of scalability when increasing the number of shots managed.…”

Section: Discussionmentioning

confidence: 99%

“…The WA1 model presented enables taking into account the inherent heterogeneity of this kind of systems and optimize its performance. On the other hand, a parallel implementation of the most time consuming services has been used 33, 51. It allows these services to run in parallel, both in cluster or shared‐memory architectures, achieving almost lineal speedup figures.…”

Section: Discussionmentioning

confidence: 99%

“…In our case the signature of a shot will be built using its keyframe. Feature comparison ( FC ): It compares two signatures and returns a measure of similarity between them. Parallel algorithms have been developed to take advantage of compound resources, such as clusters or supercomputers. In this way, parallel versions 33, 51 have been used to provide the SBD and FC services, respectively. These algorithms are based on MPI 52 enabling its use on both shared and distributed memory machines. …”

Section: Gcvirmentioning

confidence: 99%

See 2 more Smart Citations

GCViR: grid content‐based video retrieval with work allocation brokering

Toharia

Sánchez

Bosque

et al. 2010

Concurrency and Computation

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SUMMARYNowadays TV channels generate a large amount of video data each day. A very huge number of videos of news, shows, series, movies, and so on have to be stored with the aim of being accessed later on. Moreover, channels have a clear need of sharing videos so as to settle a real collaboration among them that minimizes the cost of information acquisition. These features demand a huge storage capacity and a sharing information environment. Both requirements can be solved by using grid computing. It provides both computing and storage capacities to store that great volume of data required, as well as the resource sharing capabilities for the cooperation of different TV channels. This paper presents a video retrieval system that covers these needs and suggests a work allocation (WA) broker to improve the performance of video accesses. An evaluation shows the feasibility and the scalability of this approach showing the benefits of the WA made to store and retrieve large video data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Gcvirmentioning

confidence: 99%

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GCViR: grid content‐based video retrieval with work allocation brokering

Toharia

Sánchez

Bosque

et al. 2010

Concurrency and Computation

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“…J.L. Bosque et al [15] proposed a parallel CBIR implementation on cluster. The authors presented a dynamic, distributed, global and highly scalable load balancing algorithm to improve performance.…”

Section: A Related Parallel Cbir In the Literaturementioning

confidence: 99%

Parallelization and optimization of a CBVIR system on multi-core architectures

Miao

Chen²,

Li³

et al. 2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Technique advances have made image capture and storage very convenient, which results in an explosion of the amount of visual information. It becomes difficult to find useful information from these tremendous data. Content-based Visual Information Retrieval (CBVIR) is emerging as one of the best solutions to this problem. Unfortunately, CBVIR is a very compute-intensive task. Nowadays, with the boom of multi-core processors, CBVIR can be accelerated by exploiting multi-core processing capability. In this paper, we propose a parallelization implementation of a CBVIR system facing to server application and use some serial and parallel optimization techniques to improve its performance on an 8-core and on a 16-core systems. Experimental results show that optimized implementation can achieve very fast retrieval on the two multicore systems. We also compare the performance of the application on the two multi-core systems and give an explanation of the performance difference between the two systems. Furthermore, we conduct detailed scalability and memory performance analysis to identify possible bottlenecks in the application. Based on these experimental results and performance analysis, we gain many insights into developing efficient applications on future multicore architectures.

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“…It has been shown that such heterogeneous networks of computers (HNOCs) can realize a very high level of the aggregate performance in both generic [12,13] and remote sensing applications [14], and the pervasive availability of these resources has resulted in the progressive incorporation of the concept of grid computing [15] into remote sensing studies, with the ultimate goal of making distributed collections of data easy to access from different users. Although it is expected that heterogeneous and grid-based CBIR systems will soon represent a tool of choice in many scientific applications [16,17], very few efforts have been developed toward the design of computationally efficient CBIR systems for remote sensing image retrieval on fully heterogeneous platforms, i.e. in systems made up of distributed heterogeneous processors that communicate through links with different capacities.…”

Section: Introductionmentioning

confidence: 99%

Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis

Plaza

Paz

2009

Concurrency and Computation

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SUMMARYThe purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by latest-generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever-growing data archives. To address this need, this paper develops a new parallel CBIR system that has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub-pixel precision using spectral mixture analysis concepts, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations and a Beowulf cluster at NASA's Goddard Space Flight Center. Our experimental results indicate that the proposed parallel system can efficiently retrieve hyperspectral images from complex image databases by efficiently adapting to the underlying parallel platform on which it is run, regardless of the heterogeneity in the compute nodes and communication links that form such parallel platform.

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Parallel CBIR implementations with load balancing algorithms

Cited by 17 publications

References 36 publications

GCViR: grid content‐based video retrieval with work allocation brokering

GCViR: grid content‐based video retrieval with work allocation brokering

Parallelization and optimization of a CBVIR system on multi-core architectures

Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis

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