Sparse Coding Trees with Application to Emotion Classification
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.
Cite
Text
Chen et al. "Sparse Coding Trees with Application to Emotion Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301357Markdown
[Chen et al. "Sparse Coding Trees with Application to Emotion Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/chen2015cvprw-sparse/) doi:10.1109/CVPRW.2015.7301357BibTeX
@inproceedings{chen2015cvprw-sparse,
title = {{Sparse Coding Trees with Application to Emotion Classification}},
author = {Chen, Hsieh-Chung and Comiter, Marcus Z. and Kung, H. T. and McDanel, Bradley},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {77-86},
doi = {10.1109/CVPRW.2015.7301357},
url = {https://mlanthology.org/cvprw/2015/chen2015cvprw-sparse/}
}