Large-Scale Image Annotation using Visual Synset
(ICCV 2011)
Illustration of Visual Synsets |
| Authors |
David Tsai |
| Abstract |
| We address the problem of large-scale annotation of web images. Our approach is based on the concept of visual synset, which is an organization of images which are visually-similar and semantically-related. Each visual synset represents a single prototypical visual concept, and has an associated set of weighted annotations. Linear SVM’s are utilized to predict the visual synset membership for unseen image examples, and a weighted voting rule is used to construct a ranked list of predicted annotations from a set of visual synsets. We demonstrate that visual synsets lead to better performance than standard methods on a new annotation database containing more than 200 million im- ages and 300 thousand annotations, which is the largest ever reported. |
| Paper |
| Supplementary Material |
| Download ZIP (6.9 MB) |
| Citation |
@article{TsaiICCV11,
author = {David Tsai and Yushi Jing and Yi Liu and Henry A.Rowley and Sergey Ioffe and James M.Rehg},
title = {Large-Scale Image Annotation using Visual Synset},
journal = {ICCV},
year = {2011},
} |
| Poster |
| Download Poster (1.4 MB) |
| Data |
| Readme Part1 (959 MB) Part2 (579MB) |
| Funding |
This research is supported by:
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| Copyright |
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