Can I read Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks on EtoBox?

Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks by Hu, Yuting; Long, Zhiling; AlRegib, Ghassan is a scholarly article available to read on EtoBox.

What is Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks about?

In this paper, we propose a multi-level texture encoding and representation network (MuLTER) for texture-related applications. Based on a multi-level pooling architecture, the MuLTER network simultaneously leverages low- and high-level features to maintain both texture details and spatial information. Such a pooling architecture involves few extra parameters and keeps feature dimensions fixed despite of the changes of image sizes. In comparison with state-of-the-art texture descriptors, the MuLTER network yields higher recognition accuracy on typical texture datasets such as MINC-2500 and GTOS-mobile with a discriminative and compact representation. In addition, we analyze the impact of combining features from different levels, which supports our claim that the fusion of multi-level features efficiently enhances recognition performance. Our source code will be published on GitHub (https://github.com/olivesgatech).

Author

Hu, Yuting; Long, Zhiling; AlRegib, Ghassan

Published

2019

Language

EN