NDLI: Object recognition with image set based on kernel information entropy

Content Provider	IEEE Xplore Digital Library
Author	Anping Yang Songqiao Chen
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Anping Yang; Songqiao Chen)
Abstract	We propose a new kernel entropy component analysis approach to object recognition with image set by fusing the information entropy. Since the geometry of Symmetric Positive Definite (SPD) matrices, we model the image set with its covariance matrix (nonsingular). Thus the object recognition with image set can be model as classifying problem on the riemannian manifold space. Given the proper kernel function derived from efficient metric for SPD matrices, points lie on manifold space represented by covariance matrices can be cast into high dimensional euclidean space. In this euclidean space, traditional methods used for dimensional reduction can be applied directly. Accounting for the measurability to information of the Renyi entropy, we fuse the information entropy to the dimensional reduction progress of our method. Similar to Kernel Principal Component Analysis (KPCA), the method accomplish data dimensionality reduction by projection onto a subset of entropy preserving KPCA axes. But this subset does not need to correspond to the top eigenvalues of the kernel matrix. The positive results of experiment on datasets demonstrated effectiveness of our method.
Starting Page	1314
Ending Page	1318
File Size	160100
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Measurement dimensionality reduction kernel mapping Entropy Eigenvalues and eigenfunctions Kernel Covariance matrices image set Principal component analysis Renyi entropy covariance matrix
Content Type	Text
Resource Type	Article

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