Name
Abstract | ||
|---|---|---|
Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in many previous works. We review this assumption and claim that using the similarity of data points to a more global set of anchor points does not necessarily weaken the coding method, as long as the underlying structure of the anchor points is taken into account. We propose to capture the underlying structure by assuming a random walker over the anchor points. We also show that our method is a fast approximation to the diffusion map kernel. Experiments on various datasets show that with a knowledge of the underlying structure of anchor points, different state-of-the-art coding algorithms may boost their performance in different learning tasks by utilizing the proposed method |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2465171 | Image Processing, IEEE Transactions |
Keywords | Field | DocType |
Sparse coding,diffusion kernel,local coordinate coding | Data point,Computer vision,Tunstall coding,Locality,Pattern recognition,Support vector machine,Coding (social sciences),Artificial intelligence,Random walker algorithm,Shannon–Fano coding,Mathematics,Variable-length code | Journal |
Volume | Issue | ISSN |
PP | 99 | 1057-7149 |
Citations | PageRank | References |
1 | 0.35 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amirreza Shaban | 1 | 48 | 5.60 |
| Hamid R. Rabiee | 2 | 336 | 41.77 |
| Mahyar Najibi | 3 | 70 | 7.63 |
| Safoora Yousefi | 4 | 1 | 0.35 |