Improving Speed and Accuracy of Image Retrieval using Elastic Search and Features Nearest Neighbor Search
M.R. Sundarakumar1, G.Mahadevan2
1M R Sundarakumar, Research Scholar, Department of CSE, AMC Engineering College, Bangalore, India.
2Dr.G.Mahadevan, Professor, Department of CSE, AMC Engineering College, Bangalore, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 7 August 2019. | Manuscript published on 30 September 2019. | PP: 909-913 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4089098319/19©BEIESP | DOI: 10.35940/ijrte.C4089.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A developing interest had shown as late in structure closest neighbor search arrangements inside Elastic search–one of the most well-known full-content web indexes. In this paper, we focus explicitly around Elastic search and Features Nearest Neighbor search (ESFNNS), which accomplishes sensitive speed-ups over the current term coordinate gauge. Features Nearest Neighbor search performs the image retrieval, which integrates the features of color, shape, and texture. This will engage an Elastic search with the capacity of quick data retrieval and accuracy when compared to the FENSHSES method.
Keywords: Elastic search, Nearest Neighbor Search, Hamming space, Content Based Image Retrieval.
Scope of the Article: Optical and High-Speed Access Networks.