Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1969
Title: Distinctive Interest Point Selection for Efficient Near-duplicate Image Retrieval
Authors: Yıldız, Burak
Demirci, Muhammed Fatih
Keywords: Near-duplicate image retrieval
near-duplicate image detection
density map generation
interest point selection
Publisher: IEEE
Source: Yıldız, B., & Demirci, M. F. (2016, March). Distinctive interest point selection for efficient near-duplicate image retrieval. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp. 49-52). IEEE.
Abstract: Distinctive subset of the interest points creation for near-duplicate image retrieval is significant in two terms. The former is that the query time decreases reasonably. The latter is that using the distinctive subsets performs better than the ordinary subsets. In this paper, we focus on the creation of such subsets for effective near-duplicate retrieval and propose a novel interest point selection method. In this method, the distinctive subset is created with a ranking according to a density map calculated from the interest points. We examined a number of experiments to show the performance of the proposed method and we got a convincing result of 95.46% recall while the precision is still 96.04%.
Description: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (2016 : Santa Fe, NM)
URI: https://ieeexplore.ieee.org/document/7459172
https://hdl.handle.net/20.500.11851/1969
ISBN: 978-1-4673-9919-7
ISSN: 1550-5782
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record



CORE Recommender

SCOPUSTM   
Citations

2
checked on Mar 23, 2024

Page view(s)

28
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.