Discriminative features and classification methods for accurate classification

Michael P. Dessauer; Sumeet Dua

doi:10.1117/12.853267

15 April 2010 Discriminative features and classification methods for accurate classification

Michael P. Dessauer, Sumeet Dua

Proceedings Volume 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV; 77040L (2010) https://doi.org/10.1117/12.853267
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Automated classification and tracking approaches suffer from the high-dimensionality of the data and information space, which frequently rely upon both discriminative feature selection and efficient, accurate supervised classification strategies. Feature selection strategies have the benefit of representing the data in a modified reduced space to improve the efficacy of data mining, machine learning, and computer vision approaches. We have developed feature-selection methods involving feature ranking and assimilation to discover reduced feature sets that produce accurate results in classification for automated classifiers with significant specificity and sensitivity. We have tested a wide range of spatial, texture, and wavelet-based feature sets for electro-optical (EO) aerial imagery and infrared (IR) land-based image sequences on several machine-learning algorithms for classification for performance evaluation and comparison. A detailed experimental evaluation is provided for the classification efficacy of the features and classifiers on the particular data sets, and is accompanied by a discussion of the particular success or failure. In the second section, we detail our novel feature set that combines moment and edge descriptors and produces high, robust accuracy when evaluated for classification. Our method leverages information previously calculated in the detection stage, which includes wavelet decomposition and texture statistics. We demonstrate the results of our feature set implementation and discuss methods for creating classifier decision rules to choose a particular classification algorithm dependent on certain operating conditions or data types adaptively.

Citation Download Citation

Michael P. Dessauer and Sumeet Dua "Discriminative features and classification methods for accurate classification", Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 77040L (15 April 2010); https://doi.org/10.1117/12.853267

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