Kernel Codebooks for Scene Categorization
Abstract
This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.
Cite
Text
van Gemert et al. "Kernel Codebooks for Scene Categorization." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88690-7_52Markdown
[van Gemert et al. "Kernel Codebooks for Scene Categorization." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/vangemert2008eccv-kernel/) doi:10.1007/978-3-540-88690-7_52BibTeX
@inproceedings{vangemert2008eccv-kernel,
title = {{Kernel Codebooks for Scene Categorization}},
author = {van Gemert, Jan C. and Geusebroek, Jan-Mark and Veenman, Cor J. and Smeulders, Arnold W. M.},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {696-709},
doi = {10.1007/978-3-540-88690-7_52},
url = {https://mlanthology.org/eccv/2008/vangemert2008eccv-kernel/}
}