Learning optimal features for visual pattern recognition

Kai Labusch; Udo Siewert; Thomas Martinetz; Erhardt Barth

doi:10.1117/12.713371

15 February 2007 Learning optimal features for visual pattern recognition

Kai Labusch, Udo Siewert, Thomas Martinetz, Erhardt Barth

Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 64920B (2007) https://doi.org/10.1117/12.713371
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

The optimal coding hypothesis proposes that the human visual system has adapted to the statistical properties of the environment by the use of relatively simple optimality criteria. We here (i) discuss how the properties of different models of image coding, i.e. sparseness, decorrelation, and statistical independence are related to each other (ii) propose to evaluate the different models by verifiable performance measures (iii) analyse the classification performance on images of handwritten digits (MNIST data base). We first employ the SPARSENET algorithm (Olshausen, 1998) to derive a local filter basis (on 13 × 13 pixels windows). We then filter the images in the database (28 × 28 pixels images of digits) and reduce the dimensionality of the resulting feature space by selecting the locally maximal filter responses. We then train a support vector machine on a training set to classify the digits and report results obtained on a separate test set. Currently, the best state-of-the-art result on the MNIST data base has an error rate of 0,4%. This result, however, has been obtained by using explicit knowledge that is specific to the data (elastic distortion model for digits). We here obtain an error rate of 0,55% which is second best but does not use explicit data specific knowledge. In particular it outperforms by far all methods that do not use data-specific knowledge.

Citation Download Citation

Kai Labusch, Udo Siewert, Thomas Martinetz, and Erhardt Barth "Learning optimal features for visual pattern recognition", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64920B (15 February 2007); https://doi.org/10.1117/12.713371

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