DocumentCode :
981286
Title :
Principal components null space analysis for image and video classification
Author :
Vaswani, Namrata ; Chellappa, Rama
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
Volume :
15
Issue :
7
fYear :
2006
fDate :
7/1/2006 12:00:00 AM
Firstpage :
1816
Lastpage :
1830
Abstract :
We present a new classification algorithm, principal component space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i", an Mi-dimensional subspace along which the class\´ intraclass variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class\´ mean in the class\´ ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video-an action retrieval problem and abnormal activity detection.
Keywords :
image classification; principal component analysis; video signal processing; approximate null space; classification error probability; face recognition; image classification; object recognition; principal components null space analysis; subspace linear discriminant analysis; video classification; Algorithm design and analysis; Classification algorithms; Covariance matrix; Error probability; Image analysis; Kernel; Null space; Object recognition; Principal component analysis; Upper bound; Algorithms; Artificial Intelligence; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Pattern Recognition, Automated; Principal Component Analysis; Signal Processing, Computer-Assisted; Video Recording;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2006.873449
Filename :
1643691
Link To Document :
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