Name
Abstract | ||
|---|---|---|
A reference-based algorithm for scene image categorization is presented in this letter. In addition to using a reference-set for images representation, we also associate the reference-set with training data in sparse codes during the dictionary learning process. The reference-set is combined with the reconstruction error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. After dictionaries are constructed, Locality-constrained Linear Coding (LLC) features of images are extracted. Then, we represent each image feature vector using the similarities between the image and the reference-set, leading to a significant reduction of the dimensionality in the feature space. Experimental results demonstrate that our method achieves outstanding performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/LSP.2012.2228852 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
llc,image representation,dictionary learning process,k-svd,pattern recognition,reference-set,objective function,locality-constrained linear coding,sparse code,dictionary learning,image analysis,scene image categorization,feature extraction,image classification,reference-based scheme,image feature vector,feature extracton,singular value decomposition | Computer vision,Feature vector,Automatic image annotation,K-SVD,Pattern recognition,Feature detection (computer vision),Feature (computer vision),Computer science,Image texture,Image processing,Feature extraction,Artificial intelligence | Journal |
Volume | Issue | ISSN |
20 | 1 | 1070-9908 |
Citations | PageRank | References |
14 | 0.66 | 6 |
Authors | ||
5 | ||