Image Clustering using Restricted Boltzman Machine

TL;DR

This paper introduces a novel image clustering method using Restricted Boltzmann Machines (RBMs) combined with hierarchical clustering, demonstrating superior performance on benchmark datasets compared to traditional algorithms.

Contribution

The work proposes a new RBM-based approach for image clustering that includes training universal and adapted RBMs, and generating embeddings for improved clustering accuracy.

Findings

Outperforms k-means, spectral clustering, and Rank-order on benchmark datasets

Uses a two-step RBM training process for better class-specific embeddings

Achieves higher clustering accuracy on MS-Celeb-1M and DeepFashion datasets

Abstract

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.