# Multi-visual pattern mining algorithm based on variational inference Gaussian mixture and pattern activation response map model

## TL;DR

This paper introduces a new algorithm for mining visual patterns in images that improves frequency and discriminability compared to traditional methods.

## Contribution

The novel approach combines variational inference Gaussian mixture models with pattern activation response maps to enhance multi-visual pattern mining.

## Key findings

- The algorithm achieved 92.81% frequency on the CIFAR-10 dataset with a similarity threshold of 0.866.
- On the Travel dataset, it reached 95.36% classification accuracy and 94.17% F1 value.
- The method outperforms traditional algorithms in balancing frequency and discriminability.

## Abstract

Multi-visual pattern mining plays an important role in image classification, retrieval, and other fields. A multi visual pattern mining algorithm based on variational inference Gaussian mixture model and pattern activation response graph is introduced to address the issues of insufficient frequency and discriminability faced by traditional algorithms. The innovation of this algorithm lies in combining variational inference Gaussian mixture model with pattern activation response graph. The former solves the limitation of manually presetting the number of modes in traditional methods by determining the optimal number of modes to ensure frequency. The latter improves discriminability by capturing key areas of the image, solving the problem of traditional algorithms being difficult to balance the two and distinguish multiple patterns within the same category. The results showed that in quantitative analysis, the algorithm had a high frequency of 92.81% when the similarity threshold was 0.866 on the Canadian Institute for Advanced Research-10 dataset. On the Travel dataset, the classification accuracy and F1 value were as high as 95.36% and 94.17%, respectively, which were significantly higher than other algorithms. The proposed multi-visual pattern mining algorithm has high frequency and discriminability, which can provide a more comprehensive visual representation and help better mine images of the same category but different visual patterns. This algorithm provides technical support for image classification and retrieval.