Name
Abstract | ||
|---|---|---|
The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25393-0_26 | ISNN |
Keywords | Field | DocType |
Image tagging,Tag completion,Local learning,Gradient descent | Gradient descent,Mean squared prediction error,Local learning,Pattern recognition,Iterative method,Computer science,Artificial intelligence,Linear function,Machine learning | Journal |
Volume | ISSN | Citations |
abs/1508.04224 | 0302-9743 | 9 |
PageRank | References | Authors |
0.48 | 30 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyan Wang | 1 | 11 | 0.84 |
| Yihua Zhou | 2 | 52 | 2.16 |
| Haoxiang Wang | 3 | 276 | 15.25 |
| Xiaohong Yang | 4 | 9 | 0.82 |
| Feng Yang | 5 | 9 | 0.48 |
| Austin Peterson | 6 | 9 | 0.48 |