Abstract
We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding, while scene parsing is performed by a resonator network. The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial search space during factorization. We find that for this problem the resonator network is capable of fast and accurate vector factorization, and we develop a confidence-based metric that assists in tracking the convergence of the resonator network.
Abstract (translated)
我们提出了一个基于编码图像稀疏、潜在特征表示的视觉场景分析和识别系统。该系统将图像稀疏表示编码为高维向量，然后通过分解为解析场景内容。稀疏特征表示通过卷积稀疏编码从图像统计信息中学习，而场景解析由共振器网络完成。将稀疏编码与共振器网络相结合可以增加分布式表示的容量，并在分解过程中减少组合搜索空间中的碰撞。我们发现，对于这个问题，共振器网络能够实现快速和准确的向量分解，并且我们开发了一个基于信心的度量来协助跟踪共振器网络的收敛。
URL
https://arxiv.org/abs/2404.19126
