Low-level feature image retrieval using representative images from minimum spanning tree clustering

Khunsongkiet, Piyavach; Bootkrajang, Jakramate; Techawut, Churee

doi:10.1007/s11042-023-15605-5

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Content-based multimedia retrieval (CBMR) is increasingly important due to the growth of its use in several applications [1,7,12,22,32]. Example applications employing CBMR include content-based image search systems, video identification and misuse, and several applications in social networks such as data propagation.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Response-Aware Online ANN Search in Dynamic Datasets

Andrade

Barreiros

Rocha

et al. 2023

Preprint

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Similarity search is an key operation in Content-based multimedia retrieval(CBMR) applications. Online CBMR applications, which is the focus of thiswork, have to search in large and dynamic datasets that are updated during theexecution while offering low response times. Additionally, these applications aresubmitted to workloads that vary at runtime. The computing demands in thisscenario exceeds the processing power of a single computer, motivating the large-scale machines in the domain. Thus, in this work, we proposed a distributedmemory parallelization of similarity search that addresses the mentioned chal-lenges. Our solution employs the efficient Inverted File System with AsymmetricDistance Computation algorithm (IVFADC) algorithm as the baseline, which isextended here to support dynamic datasets. Further, we developed a dynamicresource management algorithm, called Multi-Stream Adaptation (MS-ADAPT),that is executed at run-time to change the computing resource assignment withthe goal of minimizing response times. We have evaluated our system solutionwith multiple data partitioning strategies using up to 160 compute nodes anda dataset with 344 billion multimedia descriptors. It demonstrated superlinearscalability for our Spatial-Aware data partition algorithms and MS-ADAPT out-performed the best static approach (oracle) by reducing the response times upto 32× on high-load cases.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Response-Aware Online ANN Search in Dynamic Datasets

Andrade

Barreiros

Rocha

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Large-scale response-aware online ANN search in dynamic datasets

Andrade,

Barreiros,

Rocha

et al. 2023

Cluster Comput

View full text Add to dashboard Cite

Similarity search is an key operation in Content-based multimedia retrieval (CBMR) applications. Online CBMR applications, which is the focus of this work, have to search in large and dynamic datasets that are updated during the execution while offering low response times. Additionally, these applications are submitted to workloads that vary at runtime. The computing demands in this scenario exceeds the processing power of a single computer, motivating the largescale machines in the domain. Thus, in this work, we proposed a distributed memory parallelization of similarity search that addresses the mentioned challenges. Our solution employs the efficient Inverted File System with Asymmetric Distance Computation algorithm (IVFADC) algorithm as the baseline, which is extended here to support dynamic datasets. Further, we developed a dynamic resource management algorithm, called Multi-Stream Adaptation (MS-ADAPT), that is executed at run-time to change the computing resource assignment with the goal of minimizing response times. We have evaluated our system solution with multiple data partitioning strategies using up to 160 compute nodes and a dataset with 344 billion multimedia descriptors. It demonstrated superlinear scalability for our Spatial-Aware data partition algorithms and MS-ADAPT outperformed the best static approach (oracle) by reducing the response times up to 32× on high-load cases.

show abstract

Low-level feature image retrieval using representative images from minimum spanning tree clustering

Cited by 2 publications

References 35 publications

Large-Scale Response-Aware Online ANN Search in Dynamic Datasets

Large-Scale Response-Aware Online ANN Search in Dynamic Datasets

Large-scale response-aware online ANN search in dynamic datasets

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