A Fast and Scalable Image Retrieval Algorithms by Leveraging Distributed Image Feature Extraction on MapReduce 


Vol. 42,  No. 12, pp. 1474-1479, Dec.  2015


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  Abstract

With mobile devices showing marked improvement in performance in the age of the Internet of Things (IoT), there is demand for rapid processing of the extensive amount of multimedia big data. However, because research on image searching is focused mainly on increasing accuracy despite environmental changes, the development of fast processing of high-resolution multimedia data queries is slow and inefficient. Hence, we suggest a new distributed image search algorithm that ensures both high accuracy and rapid response by using feature extraction of distributed images based on MapReduce, and solves the problem of memory scalability based on BIRCH indexing. In addition, we conducted an experiment on the accuracy, processing time, and scalability of this algorithm to confirm its excellent performance.


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  Cite this article

[IEEE Style]

H. Song, J. Lee, J. Lee, "A Fast and Scalable Image Retrieval Algorithms by Leveraging Distributed Image Feature Extraction on MapReduce," Journal of KIISE, JOK, vol. 42, no. 12, pp. 1474-1479, 2015. DOI: .


[ACM Style]

Hwan-Jun Song, Jin-Woo Lee, and Jae-Gil Lee. 2015. A Fast and Scalable Image Retrieval Algorithms by Leveraging Distributed Image Feature Extraction on MapReduce. Journal of KIISE, JOK, 42, 12, (2015), 1474-1479. DOI: .


[KCI Style]

송환준, 이진우, 이재길, "MapReduce 기반 분산 이미지 특징점 추출을 활용한 빠르고 확장성 있는 이미지 검색 알고리즘," 한국정보과학회 논문지, 제42권, 제12호, 1474~1479쪽, 2015. DOI: .


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