Name
Abstract | ||
|---|---|---|
We present Sparse Coding trees (SC-trees), a sparse coding-based framework for resolving misclassifications arising when multiple classes map to a common set of features. SC-trees are novel supervised classification trees that use node-specific dictionaries and classifiers to direct input based on classification results in the feature space at each node. We have applied SC-trees to emotion classification of facial expressions. This paper uses this application to illustrate concepts of SC-trees and how they can achieve high performance in classification tasks. When used in conjunction with a nonnegativity constraint on the sparse codes and a method to exploit facial symmetry, SC-trees achieve results comparable with or exceeding the state-of-the-art classification performance on a number of realistic and standard datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CVPRW.2015.7301357 | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
sparse coding trees,SC-trees,emotion classification,sparse coding-based framework,supervised classification trees,node-specific dictionaries,facial expressions,facial symmetry exploitation,realistic datasets,standard datasets | Computer vision,Feature vector,Pattern recognition,Computer science,Neural coding,Emotion classification,Exploit,Feature extraction,Facial expression,Facial symmetry,Artificial intelligence,Encoding (memory) | Conference |
Volume | Issue | ISSN |
2015 | 1 | 2160-7508 |
Citations | PageRank | References |
2 | 0.40 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsieh-Chung Chen | 1 | 36 | 4.85 |
| Marcus Z. Comiter | 2 | 3 | 2.44 |
| H. T. Kung | 3 | 368 | 68.24 |
| Bradley McDanel | 4 | 84 | 7.59 |