NDLI: Image retrieval based on ASIFT features in a Hadoop clustered system

Content Provider	IET Digital Library
Author	Huang, Yin Fu Wu, Huan Yu
Abstract	For image matching, the scale invariant feature transform (SIFT) algorithm is a commonly used one. They are invariant to image rotation, scale zooming, and partially invariant to change in illumination and 3D camera viewpoint. Affine SIFT (ASIFT) is an extension of SIFT, which solves the problem when images are captured at different angles. However, ASIFT has higher computational complexity than SIFT, due to a huge amount of features in the images. Therefore, in this study, a Hadoop-based image retrieval system is proposed to solve the ASIFT shortcomings of high computation by the MapReduce technology. The system uses a combination of the Bag-of-Words method and support vector machine. Finally, the experimental results verify that the proposed method is more effective than the other state-of-the-art methods for a variety of datasets.
Starting Page	138
Ending Page	146
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0229
Journal	IET Image Processing
Publisher Date	2019-10-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Camera Viewpoint Affine SIFT Affine Transforms ASIFT Features Bag-of-words Method Computational Complexity Computer Vision And Image Processing Technique Feature Extraction Hadoop Clustered System Hadoop-based Image Retrieval System Higher Computational Complexity Image Matching Image Retrieval Image Rotation Information Retrieval Technique Integral Transforms Knowledge Engineering Technique MapReduce Technology Multiprocessing System Optical, Image And Video Signal Processing Parallel Processing Scale Invariant Feature Transform Algorithm Scale Zooming Stereo Image Processing Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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