Name
Abstract | ||
|---|---|---|
Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. It has become a new research focus and many techniques have been proposed to solve this problem. In this paper, a novel semi-auto image annotation technique is proposed. The new developed method uses a label transfer mechanism to automatically recommend promising tags to each image by assigning each image a category label first. Since image representation is one of the key problems in image annotation, we utilize a sparse coding based spatial pyramid matching as an effective way to model and interpret image features. Experimental results demoustrate that the proposed method outperforms the current state-of-the-art methods on two benchmark image datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICMLC.2012.6359013 | ICMLC |
Keywords | Field | DocType |
image representation,image coding,image matching,image annotation,image features,sparse coding,spatial pyramid matching,benchmark image datasets,semiautomatic image annotation technique,image data,bag-of-features,label transfer mechanism,snow,filtering | Template matching,Automatic image annotation,Feature detection (computer vision),Pattern recognition,Computer science,Image texture,Binary image,Image retrieval,Image processing,Pyramid (image processing),Artificial intelligence | Conference |
Volume | ISSN | ISBN |
2 | 2160-133X | 978-1-4673-1484-8 |
Citations | PageRank | References |
2 | 0.35 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Zhang | 1 | 29 | 8.24 |
| Zengchang Qin | 2 | 439 | 45.46 |
| Tao Wan | 3 | 181 | 21.18 |