# Texture retrieval using periodically extended and adaptive curvelets

## TL;DR

This paper introduces two novel curvelet-based algorithms for texture retrieval optimized for constrained-memory devices, validated on multiple datasets, with a weighted variant also effective in seismic activity classification.

## Contribution

The paper proposes new curvelet-based texture retrieval algorithms and a weighted version, suitable for low-memory devices and applicable to seismic activity classification.

## Key findings

- Algorithms perform well on CUReT, Mondial-Marmi, STex-fabric datasets.
- Weighted algorithm improves seismic activity classification.
- Effectiveness confirmed through experiments.

## Abstract

Image retrieval is an important problem in the area of multimedia processing. This paper presents two new curvelet-based algorithms for texture retrieval which are suitable for use in constrained-memory devices. The developed algorithms are tested on three publicly available texture datasets: CUReT, Mondial-Marmi, and STex-fabric. Our experiments confirm the effectiveness of the proposed system. Furthermore, a weighted version of the proposed retrieval algorithm is proposed, which is shown to achieve promising results in the classification of seismic activities.