Approximative Coding Methods for Channel Representations

作者：Ofjall Kristoffer; Fel**erg Michael^*

来源：Journal of Mathematical Imaging and Vision, 2018, 60(6): 929-940.

DOI：10.1007/s10851-017-0775-8

• Visual features
• Channel representations
• Approximative density estimation
• Maximum entropy

Most methods that address computer vision problems require powerful visual features. Many successful approaches apply techniques motivated from nonparametric statistics. The channel representation provides a framework for nonparametric distribution representation. Although early work has focused on a signal processing view of the representation, the channel representation can be interpreted in probabilistic terms, e.g., representing the distribution of local image orientation. In this paper, a variety of approximative channel-based algorithms for probabilistic problems are presented: a novel efficient algorithm for density reconstruction, a novel and efficient scheme for nonlinear gridding of densities, and finally a novel method for estimating Copula densities. The experimental results provide evidence that by relaxing the requirements for exact solutions, efficient algorithms are obtained.

• 出版日期2018-7