Image Classification Using Super-Vector Coding of Local Image Descriptors
Abstract
This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a nonlinear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks.
Cite
Text
Zhou et al. "Image Classification Using Super-Vector Coding of Local Image Descriptors." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15555-0_11Markdown
[Zhou et al. "Image Classification Using Super-Vector Coding of Local Image Descriptors." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/zhou2010eccv-image/) doi:10.1007/978-3-642-15555-0_11BibTeX
@inproceedings{zhou2010eccv-image,
title = {{Image Classification Using Super-Vector Coding of Local Image Descriptors}},
author = {Zhou, Xi and Yu, Kai and Zhang, Tong and Huang, Thomas S.},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {141-154},
doi = {10.1007/978-3-642-15555-0_11},
url = {https://mlanthology.org/eccv/2010/zhou2010eccv-image/}
}