Learning Mid-Level Features from Object Hierarchy for Image Classification

Somayah Albaradei, Yang Wang, Liangliang Cao, Li-Jia Li

WACV 2014 pp. 235-240

doi:10.1109/WACV.2014.6836095 /wacv/2014/albaradei2014wacv-learning/

Abstract

We propose a new approach for constructing mid-level visual features for image classification. We represent an image using the outputs of a collection of binary classifiers. These binary classifiers are trained to differentiate pairs of object classes in an object hierarchy. Our feature representation implicitly captures the hierarchical structure in object classes. We show that our proposed approach outperforms other baseline methods in image classification.

WACV Semantic Scholar

Cite

Text

Albaradei et al. "Learning Mid-Level Features from Object Hierarchy for Image Classification." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836095

Markdown

[Albaradei et al. "Learning Mid-Level Features from Object Hierarchy for Image Classification." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/albaradei2014wacv-learning/) doi:10.1109/WACV.2014.6836095

BibTeX

@inproceedings{albaradei2014wacv-learning,
  title     = {{Learning Mid-Level Features from Object Hierarchy for Image Classification}},
  author    = {Albaradei, Somayah and Wang, Yang and Cao, Liangliang and Li, Li-Jia},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2014},
  pages     = {235-240},
  doi       = {10.1109/WACV.2014.6836095},
  url       = {https://mlanthology.org/wacv/2014/albaradei2014wacv-learning/}
}