In various verification systems, Restricted Boltzmann Machines (RBMs) have
demonstrated their efficacy in both front-end and back-end processes. In this
work, we propose the use of RBMs to the image clustering tasks. RBMs are
trained to convert images into image embeddings. We employ the conventional
bottom-up Agglomerative Hierarchical Clustering (AHC) technique. To address the
challenge of limited test face image data, we introduce Agglomerative
Hierarchical Clustering based Method for Image Clustering using Restricted
Boltzmann Machine (AHC-RBM) with two major steps. Initially, a universal RBM
model is trained using all available training dataset. Subsequently, we train
an adapted RBM model using the data from each test image. Finally, RBM vectors
which is the embedding vector is generated by concatenating the
visible-to-hidden weight matrices of these adapted models, and the bias
vectors. These vectors effectively preserve class-specific information and are
utilized in image clustering tasks. Our experimental results, conducted on two
benchmark image datasets (MS-Celeb-1M and DeepFashion), demonstrate that our
proposed approach surpasses well-known clustering algorithms such as k-means,
spectral clustering, and approximate Rank-order.

使用限制玻尔兹曼机 (Restricted Boltzmann Machines) 进行图像聚类任务，通过训练通用 RBM 模型和适应每个测试图像的 RBM 模型，生成嵌入向量用于图像聚类任务，并在实验结果中表明该方法优于 k-means、谱聚类和近似顺序排名算法。

使用受限玻尔兹曼机进行图像聚类

Image Clustering using Restricted Boltzman Machine

Whether it is object detection, model reconstruction, laser odometry, or
point cloud registration: Plane extraction is a vital component of many robotic
systems. In this paper, we propose a strictly probabilistic method to detect
finite planes in organized 3-D laser range scans. An agglomerative hierarchical
clustering technique, our algorithm builds planes from bottom up, always
extending a plane by the point that decreases the measurement likelihood of the
scan the least. In contrast to most related methods, which rely on heuristics
like orthogonal point-to-plane distance, we leverage the ray path information
to compute the measurement likelihood. We evaluate our approach not only on the
popular SegComp benchmark, but also provide a challenging synthetic dataset
that overcomes SegComp's deficiencies. Both our implementation and the
suggested dataset are available at www.github.com/acschaefer/ppe.

本文提出了一种严格的概率方法来检测有限平面，该算法通过自下而上的聚类技术从底部构建平面，利用光线路径信息计算测量似然性，不依赖于点到平面距离，可以应用于机器人系统的不同领域。