Parallel big image data retrieval by conceptualised clustering and un-conceptualised clustering Online publication date: Mon, 11-Nov-2019
by Ja-Hwung Su; Chu-Yu Chin; Jyun-Yu Li; Vincent S. Tseng
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 15, No. 1/2, 2019
Abstract: Content-based image retrieval is a hot topic which has been studied for few decades. Although there have been a number of recent studies proposed on this topic, it is still hard to achieve a high retrieval performance for big image data. To aim at this issue, in this paper, we propose a parallel content-based image retrieval method that efficiently retrieves the relevant images by un-conceptualised clustering and conceptualised clustering. For un-conceptualised clustering, the un-conceptualised image data is automatically divided into a number of sets, while the conceptualised image data is divided into multiple sets by conceptualised clustering. Based on the clustering index, the depth-first-search strategy is performed to retrieve the relevant images by parallel comparisons. Through experimental evaluations on a large image dataset, the proposed approach is shown to improve the performance of content-based image retrieval substantially in terms of efficiency.
Online publication date: Mon, 11-Nov-2019
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