Name
Abstract | ||
|---|---|---|
Bag-of-features (BoF) model based on SIFT generally assumes each descriptor is related to only one visual word of the codebook. Therefore, the potential correlation between the descriptor and other visual words is ignored. On the other hand, sparse coding through l1-norm regularization fails to generate optimal sparse representations since l1-norm regularization randomly selected one variable from a group of highly correlated variables. In this study we propose a novel bag-of-features model for image retrieval called SIFT-based Elastic sparse coding. The method utilizes a large number of SIFT descriptors to construct the codebook. The Elastic Net regression framework, which combines both l1-norm and l2-norm penalties, is then used to obtain the sparse-coefficient vector corresponding to the SIFT descriptor. Finally each image can be represented by a unified sparse-coefficient vector. Experimental results on Coil20 dataset demonstrate the consistent superiority of the proposed method over the state-of-the-art algorithms including original SIFT matching, conventional BoF strategy and BoF model based on l1-norm sparse coding. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467390 | ICIP |
Keywords | Field | DocType |
image representation,image coding,scale invariant feature transform,image matching,elastic net regression framework,l1-norm regularization,regression analysis,optimal sparse representation generation,bof model,l1-norm sparse coding,bag-of-features model,content-based image retrieval,unified sparse-coefficient vector,coil20 dataset,codebook visual word,feature extraction,image retrieval,sift-based elastic sparse coding,l1-norm penalties,bag-of-features,transforms,content-based retrieval,l2-norm penalties,vectors,sparse representation,sift matching,sift descriptors | Scale-invariant feature transform,Computer vision,Pattern recognition,Elastic net regularization,Neural coding,Computer science,Image retrieval,Feature extraction,Regularization (mathematics),Artificial intelligence,Codebook,Visual Word | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 5 |
PageRank | References | Authors |
0.40 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Shi | 1 | 51 | 5.23 |
| Zhiguo Jiang | 2 | 321 | 45.58 |
| Hao Feng | 3 | 46 | 2.21 |
| Liguo Zhang | 4 | 15 | 7.92 |