Comparative study of feature extraction algorithms for complex-valued gradient fields of digital images using linear dimensionality reduction methods

E. A. Dmitriev; V. V. Myasnikov

doi:10.1117/12.2523098

15 March 2019 Comparative study of feature extraction algorithms for complex-valued gradient fields of digital images using linear dimensionality reduction methods

E. A. Dmitriev, V. V. Myasnikov

Author Affiliations +

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104127 (2019) https://doi.org/10.1117/12.2523098
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

The paper presents an analysis of various approaches to constructing descriptions for gradient fields of digital images. The analyzed approaches are based on the well-known methods for reducing the data dimensionality, such as Principal (PCA) and Independent (ICA) Component Analysis, Linear Discriminant Analysis (LDA). We apply these methods not to original image, represented as a two-dimensional field of brightness (a halftone image), but to its secondary representation in the form of a two-dimensional gradient field, that is a complex-valued image. In this case, the approaches of using both the entire gradient field and only its phase part are analyzed. Also, the two independent ways of forming the original object final description are considered: using gradient field expansion coefficients in a derived basis and using the original author's method called model-oriented descriptors. The latter ones enable halving the number of real coefficients used in the original object description. The studies are conducted via solving face recognition problem. The effectiveness of the analyzed methods is demonstrated by applying them to images from Extended Yale Face Database B. The comparison is made using a nearest neighbor's classifier.

Citation Download Citation

E. A. Dmitriev and V. V. Myasnikov "Comparative study of feature extraction algorithms for complex-valued gradient fields of digital images using linear dimensionality reduction methods", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104127 (15 March 2019); https://doi.org/10.1117/12.2523098

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Principal component analysis

Independent component analysis

Databases

Halftones

Facial recognition systems

Image analysis

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years