Ambiguous Proximity Distribution

TL;DR

This paper introduces ambiguous proximity distributions using soft assignment of visual words, improving image classification and retrieval performance in medical datasets by enhancing the proximity distribution kernel method.

Contribution

It proposes a novel visual word contribution function to model ambiguous proximity distributions, enhancing bag-of-features image representations.

Findings

Proposed methods outperform or match state-of-the-art in medical image classification.

Ambiguous proximity distributions improve image retrieval accuracy.

The approach demonstrates robustness across different medical imaging datasets.

Abstract

Proximity Distribution Kernel is an effective method for bag-of-featues based image representation. In this paper, we investigate the soft assignment of visual words to image features for proximity distribution. Visual word contribution function is proposed to model ambiguous proximity distributions. Three ambiguous proximity distributions is developed by three ambiguous contribution functions. The experiments are conducted on both classification and retrieval of medical image data sets. The results show that the performance of the proposed methods, Proximity Distribution Kernel (PDK), is better or comparable to the state-of-the-art bag-of-features based image representation methods.