Name
Title | ||
|---|---|---|
Nonnegative complementary prototype representation based classifier for object recognition |
Abstract | ||
|---|---|---|
Based on the assumption that a query image can be represented as the nonnegative combination of the generated class-specific prototypes, a simple but effective classifier, non-negative complementary prototype representation based classifier (NCPRC), is proposed for object recognition. First, the query-dependent class prototypes are constructed using least squares. Second, we apply nonnegative least squares to estimate the coefficients of the prototypes. Finally, the identity of a query image is disclosed by the farthest rule with complementary prototypes. The proposed classifier doesn't need the delicate parameter tuning. Experiments on four standard image datasets validate the superiority of our approach over several state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BMSB.2013.6621770 | BMSB |
Keywords | Field | DocType |
standard image dataset,image representation,generated class-specific prototype,prototype generation,query image representation,nonnegative least squares,image reconstruction,parameter tuning,least squares approximations,nonnegative complementary prototype representation based classifier,image classification,nonnegative least square,object recognition,query-dependent class prototype,ncprc,the farthest rule,face recognition,accuracy,databases,prototypes | Iterative reconstruction,Least squares,Computer vision,Pattern recognition,Computer science,Image representation,Artificial intelligence,Contextual image classification,Classifier (linguistics),Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
null | null | 2155-5044 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||