Name
Title | ||
|---|---|---|
A hybrid approach combining extreme learning machine and sparse representation for image classification |
Abstract | ||
|---|---|---|
Two well-known techniques, extreme learning machine (ELM) and sparse representation based classification (SRC) method, have attracted significant attention due to their respective performance characteristics in computer vision and pattern recognition. In general, ELM has speed advantage and SRC has accuracy advantage. However, there also remain drawbacks that limit their practical application. Actually, in the field of image classification, ELM performs extremely fast while it cannot handle noise well, whereas SRC shows notable robustness to noise while it suffers high computational cost. In order to incorporate their respective advantages and also overcome their respective drawbacks, this work proposes a novel hybrid approach combining ELM and SRC for image classification. The new approach is applied to handwritten digit classification and face recognition, experiments results demonstrate that it not only outperforms ELM in classification accuracy but also has much less computational complexity than SRC. Display Omitted HighlightsIt is combined with Extreme Learning Machine (ELM) and Sparse Representation based Classification (SRC) method.Though analysis of ELM output, we give a criteria to estimate ELM misclassified images.ELM misclassified images are further classified by SRC method.It outperforms ELM in classification accuracy while partially inheriting the high speed of ELM.It has much less computational complexity than SRC while keeping the high accuracy of SRC. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.engappai.2013.05.012 | Eng. Appl. of AI |
Keywords | Field | DocType |
respective drawback,face recognition,hybrid approach,accuracy advantage,high computational cost,respective advantage,computational complexity,image classification,handwritten digit classification,extreme learning machine,classification accuracy,sparse representation,respective performance characteristic | Facial recognition system,Pattern recognition,Extreme learning machine,Computer science,Sparse approximation,Robustness (computer science),Artificial intelligence,Contextual image classification,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
27 | C | 0952-1976 |
Citations | PageRank | References |
23 | 0.77 | 39 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minxia Luo | 1 | 119 | 9.97 |
| Kai Zhang | 2 | 686 | 26.59 |