NDLI: Comparative Study on Dimensionality Reduction in Large-Scale Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Bo Cheng Li Zhuo Jing Zhang
Copyright Year	2013
Description	Author affiliation: Signal & Inf. Process. Lab., Beijing Univ. of Technol., Beijing, China (Bo Cheng; Li Zhuo; Jing Zhang)
Abstract	Dimensionality reduction plays a significant role for the performance of large-scale image retrieval. In this paper, various dimensionality reduction methods are compared to validate their own performance in image retrieval. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and HSV (Hue, Saturation, Value) histogram are extracted as image features. Second, the Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), Local Fisher Discriminant Analysis (LFDA), Isometric Mapping (ISOMAP), Locally Linear Embedding (LLE), and Locality Preserving Projections (LPP) are respectively applied to reduce the dimensions of SIFT feature descriptors and color information, which can be used to generate vocabulary trees. Finally, through setting the match weights of vocabulary trees, large-scale image retrieval scheme is implemented. By comparing multiple sets of experimental data from several platforms, it can be concluded that dimensionality reduction method of LLE and LPP can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well.
Starting Page	445
Ending Page	450
File Size	613841
Page Count	6
File Format	PDF
e-ISBN	9781479921713
DOI	10.1109/ISM.2013.86
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Image retrieval Principal component analysis Vocabulary Manifolds Vegetation Histograms vocabulary tree Large-scale image retrieval dimensionality reduction Scale Invariant Feature Transform HSV histogram
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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