In this paper, we propose a novel approach for feature generation by appropriately fusing KAZE and SIFT features. We then use this feature set along with Minimal Complexity Machine(MCM) for object classification. We show that KAZE and SIFT features are complementary. Experimental results indicate that an elementary integration of these techniques can outperform the state-of-the-art approaches.
Cite
@article{arxiv.1505.05240,
title = {Benchmarking KAZE and MCM for Multiclass Classification},
author = {Siddharth Srivastava and Prerana Mukherjee and Brejesh Lall},
journal= {arXiv preprint arXiv:1505.05240},
year = {2015}
}