Study of sparse features in image recognition

Jun Sun; Qing Zhuo; Wenyuan Wang

doi:10.1117/12.441466

21 September 2001 Study of sparse features in image recognition

Jun Sun, Qing Zhuo, Wenyuan Wang

Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441466
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

Feature extraction is very important to pattern recognition. For many image recognition tasks, it is very hard to directly extract the explicit geometrical features of the images. In this case, global feature extraction is often used. Principal Component Analysis (PCA) is a typical global feature extraction method. However, PCA assumes the image population as Gaussian distribution and produces a set of compact features, which are the coefficients of the basis functions with largest eigenvalues. Compared with compact features of PCA, sparse features seem more attractive for recognition tasks. In this paper, three algorithms that produce sparse feature are studied. Independent Component Analysis (ICA) and sparse coding (SP) can describe non-Gaussian distribution. The discriminatory sparse coding (DSP) is a variation of SP, which incorporates class label information of the training samples. Experiments results of face recognition show sparse features have more advantage over compact features. DSP gets the best results for its clustering property of the features.

Citation Download Citation

Jun Sun, Qing Zhuo, and Wenyuan Wang "Study of sparse features in image recognition", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441466

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