Texture Retrieval in the Wild through detection-based attributes

TL;DR

This paper demonstrates that a texel-based descriptor, Texel-Att, effectively captures textile textures in wild conditions, outperforming other methods even under severe distortions like down-sampling.

Contribution

The study shows that Texel-Att is robust for texture retrieval in wild scenarios, especially under extreme distortions, advancing texture retrieval techniques.

Findings

Texel-Att outperforms existing descriptors under severe distortions.

Robustness of texel-based representation in wild conditions.

Effective retrieval even with down-sampling by a factor of 10.

Abstract

Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest). In this paper we show that a texel-based representation fits well with this task. In particular, we refer to Texel-Att, a recent texel-based descriptor which has shown to capture fine grained variations of a texture, for retrieval purposes. After a brief explanation of Texel-Att, we will show in our experiments that this descriptor is robust to distortions resulting from acquisitions in the wild by setting up an experiment in which textures from the ElBa (an Element-Based texture dataset) are artificially distorted and then used to retrieve the original image. We compare our approach with existing descriptors using a simple ranking framework based on distance functions. Results show that even under extreme conditions (such as a down-sampling with a factor of 10), we perform better than alternative approaches.