# Completed Local Derivative Pattern for Rotation Invariant Texture Classification

## TL;DR

This paper introduces the completed local derivative pattern (CLDP), a new texture descriptor that captures directional variations in textures, improving classification accuracy while maintaining low computational complexity.

## Contribution

The paper proposes CLDP, a novel texture descriptor that encodes directional derivatives, enhancing texture classification performance over existing uni-scale methods.

## Key findings

- CLDP outperforms state-of-the-art uni-scale texture descriptors.
- CLDP achieves comparable results to multi-scale descriptors.
- Experimental validation on Outex database confirms effectiveness.

## Abstract

In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative pattern, reflects the directional smoothness of local textures without increasing computation complexity. Experimental results on the Outex database show that CLDP, as a uni-scale pattern, outperforms uni-scale state-of-the-art texture descriptors on texture classification and has comparable performance with multi-scale texture descriptors.