Visual keyword image retrieval based on synergetic neural network for web-based image search
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 127-142 |
| Journal / Publication | Real-Time Systems |
| Volume | 21 |
| Issue number | 1-2 |
| Publication status | Published - Jul 2001 |
Link(s)
Abstract
Feature extraction and similarity measure are two basic key issues in image retrieval. Combining the advantages of SNN in image recognition and selective attention for image retrieval, a novel visual keywords-driven image retrieval approach based on these properties has been proposed. By using a predefined set of visual keywords as prototype patterns stored with the SNN and then measuring the degree of similarity of the stored images to the visual keywords, we show that such a visual keyword driven SNN can provide the framework for image indexing or retrieval which is scalable, robust and efficient for web-based search.
Research Area(s)
- Affine-invariant feature, Synergetic neural network, Trademark image retrieval, Visual keywords, web-based multimedia search
Citation Format(s)
Visual keyword image retrieval based on synergetic neural network for web-based image search. / Zhao, Tong; Tang, Lilian H.; Ip, Horace H.S.; Qi, Feihu.
In: Real-Time Systems, Vol. 21, No. 1-2, 07.2001, p. 127-142.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
