Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering

Siquan Yu,Zhi Han,Yandong Tang,Chengdong Wu,Yong Li,Jiaxin Liu,Yumin Cheng

doi:10.1155/2021/3742536

Abstract

Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.

Highlights

Clustering is a basic unsupervised learning problem whose purpose is to divide data into several subgroups
Many attempts have been dedicated to developing suitable clustering feature extracting techniques such as manually designed feature descriptors, including Bag of feature (BOW) [7], Histogram of Oriented Gradient (HOG) [8], Principal Component Analysis (PCA) [9], and Scale-Invariant Feature Transform (SIFT) [10]
To overcome the problems of DAC, we present an image clustering representation learning method based on autoencoder (AE) [21] and deep adaptive image clustering (DAC) [20]

Summary

Introduction

Clustering is a basic unsupervised learning problem whose purpose is to divide data into several subgroups. The reliable clustering method of image data is still an outstanding problem [3, 4]. E traditional image clustering methods group data on handcrafted features and treat feature extraction and clustering separately [6]. Based on this insight, many attempts have been dedicated to developing suitable clustering feature extracting techniques such as manually designed feature descriptors, including Bag of feature (BOW) [7], Histogram of Oriented Gradient (HOG) [8], Principal Component Analysis (PCA) [9], and Scale-Invariant Feature Transform (SIFT) [10]. How to establish an effective feature representation is a crucial problem that needs to be solved in image clustering

Objectives

Methods

Conclusion