Object recognition using hybrid boosting method
Department of Computer Engineering, Bahria University, Islamabad, Pakistan
- Published
- Accepted
- Subject Areas
- Computer Vision
- Keywords
- PCA, caltech-4, discriminative
- Copyright
- © 2016 Ashfaq
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Object recognition using hybrid boosting method. PeerJ Preprints 4:e2220v1 https://doi.org/10.7287/peerj.preprints.2220v1
Abstract
Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.
Author Comment
This is a novel hybrid method using boosting algorithms for image classification.