Document Detail

Non-negative Local Coordinate Factorization for Image Representation.
MedLine Citation:
PMID:  23076045     Owner:  NLM     Status:  Publisher    
Recently Non-negative Matrix Factorization (NMF) has become increasingly popular for feature extraction in computer vision and pattern recognition. NMF seeks for two non-negative matrices whose product can best approximate the original matrix. The non-negativity constraints lead to sparse, parts-based representations which can be more robust than non-sparse, global features. To obtain more accurate control over the sparseness, in this paper, we propose a novel method called Non-negative Local Coordinate Factorization (NLCF) for feature extraction. NLCF adds a local coordinate constraint into the standard NMF objective function. Specifically, we require that the learned basis vectors be as close to the original data points as possible. In this way, each data point can be represented by a linear combination of only few nearby basis vectors, which naturally leads to sparse representation. Extensive experimental results suggest that the proposed approach provides a better representation and achieves higher accuracy in image clustering.
Y Chen; J Zhang; D Cai; W Liu; X He
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-12
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  -     ISSN:  1941-0042     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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