Paper Info
Title
Object Recognition With Image Set Based On Kernel Information Entropy
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.
Year
Venue
Keywords
2015
2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD)
image set, Renyi entropy, covariance matrix, kernel mapping, dimensionality reduction
Field
DocType
Citations 
Entropy estimation,Pattern recognition,Kernel embedding of distributions,Computer science,Rényi entropy,Quantum relative entropy,Kernel principal component analysis,Artificial intelligence,Principle of maximum entropy,Variable kernel density estimation,Kernel (statistics)
Conference
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Anping Yang100.34
Songqiao Chen25811.12