Year: 2015
Type: preprint
Abstract: This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set. In this way, the vector quantization output can keep as much information of the class label as possible. An objective function is constructed and we also developed an iterative algorithm to minimize it. The new method is evaluated on bag-of-features based image classification problem.
Source: arXiv (Cornell University)
Authors: Yang, Lan, Wang, Jingbin, Tu, Yujin, Mahapatra, Prarthana, Cardoso, Nelson
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2248361083