Name
Abstract | ||
|---|---|---|
When implementing real-world computer vision systems, researchers can use mid-level representations as a tool to adjust the trade-off between accuracy and efficiency. Unfortunately, existing mid-level representations that improve accuracy tend to decrease efficiency, or are specifically tailored to work well within one pipeline or vision problem at the exclusion of others. We introduce a novel, efficient mid-level representation that improves classification efficiency without sacrificing accuracy. Our Exemplar Codes are based on linear classifiers and probability normalization from extreme value theory. We apply Exemplar Codes to two problems: facial attribute extraction and tattoo classification. In these settings, our Exemplar Codes are competitive with the state of the art and offer efficiency benefits, making it possible to achieve high accuracy even on commodity hardware with a low computational budget. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WACV.2014.6836099 | Applications of Computer Vision |
Keywords | Field | DocType |
computer vision,face recognition,feature extraction,image classification,image representation,probability,classification efficiency,exemplar codes,extreme value theory,facial attribute extraction,linear classifiers,mid-level representations,probability normalization,real-world computer vision systems,tattoo classification,tattoo recognition | Computer vision,Vision problem,Normalization (statistics),Pattern recognition,Computer science,Extreme value theory,Support vector machine,Feature extraction,Artificial intelligence,Commodity hardware,Machine learning | Conference |
ISSN | Citations | PageRank |
2472-6737 | 4 | 0.48 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael J. Wilber | 1 | 86 | 7.37 |
| Rudd Ethan M. | 2 | 151 | 9.40 |
| Brian Heflin | 3 | 13 | 1.79 |
| Yui-Man Lui | 4 | 4 | 0.48 |
| Terrance E. Boult | 5 | 1901 | 223.30 |