提出混合球面随机嵌入网络，提升纹理识别表征能力
MIXER: Mixed Hyperspherical Random Embedding Neural Network for Texture Recognition
- 用球面随机嵌入捕捉通道内与通道间关系
- 双分支结构结合新优化目标，增强纹理表征
- 在多个纹理数据集上表现优异，适合纹理分析任务
随机化神经网络在纹理识别任务中持续表现出色，有效融合了传统方法与学习型方法的优势。然而，现有方法主要聚焦于跨信息预测的改进，对整体随机网络架构的创新有限。本文提出Mixer，一种新型随机神经网络，用于纹理表征学习。其核心是将超球面随机嵌入与双分支学习模块结合，以捕捉通道内与通道间的关系，并通过新构建的优化问题进一步丰富纹理表征。实验结果表明，该方法在多个具有不同特征与挑战的纯纹理基准测试中均取得显著成效。源代码将在发表后公开。
原文摘要 · Abstract (English)
Randomized neural networks for representation learning have consistently achieved prominent results in texture recognition tasks, effectively combining the advantages of both traditional techniques and learning-based approaches. However, existing approaches have so far focused mainly on improving cross-information prediction, without introducing significant advancements to the overall randomized network architecture. In this paper, we propose Mixer, a novel randomized neural network for texture representation learning. At its core, the method leverages hyperspherical random embeddings coupled with a dual-branch learning module to capture both intra- and inter-channel relationships, further enhanced by a newly formulated optimization problem for building rich texture representations. Experimental results have shown the interesting results of the proposed approach across several pure texture benchmarks, each with distinct characteristics and challenges. The source code will be available upon publication.
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