scholarly journals A Hybrid Autoencoder Network for Unsupervised Image Clustering

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 122 ◽  
Author(s):  
Pei-Yin Chen ◽  
Jih-Jeng Huang
Keyword(s):  
Feature Learning ◽  
Image Data ◽  
Image Clustering ◽  
Great Success ◽  
Distance Metrics ◽  
Clustering Methods ◽  
Unsupervised Feature Learning ◽  
Agglomerative Clustering ◽  
Convolutional Autoencoder ◽  
Important Field

Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment.

Pattern Mining and Clustering on Image Databases

2008 ◽  
pp. 187-212
Author(s):  
Marinette Bouet ◽  
Pierre Gançarski ◽  
Marie-Aude Aufaure ◽  
Omar Boussaïd
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Image Data ◽  
Research Direction ◽  
Relevant Information ◽  
Frequent Pattern ◽  
Image Clustering ◽  
Image Mining ◽  
Clustering Methods ◽  
New Research

Analysing and mining image data to derive potentially useful information is a very challenging task. Image mining concerns the extraction of implicit knowledge, image data relationships, associations between image data and other data or patterns not explicitly stored in the images. Another crucial task is to organize the large image volumes to extract relevant information. In fact, decision support systems are evolving to store and analyse these complex data. This paper presents a survey of the relevant research related to image data processing. We present data warehouse advances that organize large volumes of data linked with images and then, we focus on two techniques largely used in image mining. We present clustering methods applied to image analysis and we introduce the new research direction concerning pattern mining from large collections of images. While considerable advances have been made in image clustering, there is little research dealing with image frequent pattern mining. We shall try to understand why.

scholarly journals An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qing Ye ◽  
Changhua Liu
Keyword(s):  
Fault Diagnosis ◽  
Deep Structure ◽  
Feature Learning ◽  
Unsupervised Feature Learning ◽  
Feature Generation ◽  
Model Based ◽  
Deep Feature ◽  
Convolutional Autoencoder ◽  
Diagnostic Framework ◽  
Sample Set

Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE) is proposed and applied in the stage of feature generation of diagnostic framework. Firstly, raw vibration signals are normalized and segmented into sample set by sliding window. Secondly, deep features are, respectively, extracted from reshaped form of raw sample set and spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). During the training process, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are feed into the classification model based on deep structure of neural network (DNN) with softmax. The effectiveness of the proposed approach is evaluated in fault diagnosis of automobile main reducer. The results produced in contrastive analysis demonstrate that the diagnostic framework based on parallel unsupervised feature learning and deep structure of classification can effectively enhance the robustness and enhance the identification accuracy of operation conditions by nearly 8%.

Pattern Mining and Clustering on Image Databases

2009 ◽  
pp. 60-85
Author(s):  
Marinette Bouet ◽  
Pierre Gançarski ◽  
Marie-Aude Aufaure ◽  
Omar Boussaïd
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Image Data ◽  
Research Direction ◽  
Relevant Information ◽  
Frequent Pattern ◽  
Image Clustering ◽  
Image Mining ◽  
Clustering Methods ◽  
New Research

Analysing and mining image data to derive potentially useful information is a very challenging task. Image mining concerns the extraction of implicit knowledge, image data relationships, associations between image data and other data or patterns not explicitly stored in the images. Another crucial task is to organise the large image volumes to extract relevant information. In fact, decision support systems are evolving to store and analyse these complex data. This chapter presents a survey of the relevant research related to image data processing. We present data warehouse advances that organise large volumes of data linked with images, and then we focus on two techniques largely used in image mining. We present clustering methods applied to image analysis, and we introduce the new research direction concerning pattern mining from large collections of images. While considerable advances have been made in image clustering, there is little research dealing with image frequent pattern mining. We will try to understand why.

Pattern Mining and Clustering on Image Databases

2008 ◽  
pp. 254-279
Author(s):  
Marinette Bouet ◽  
Pierre Gançarski ◽  
Omar Boussaïd
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Image Data ◽  
Research Direction ◽  
Relevant Information ◽  
Frequent Pattern ◽  
Image Clustering ◽  
Image Mining ◽  
Clustering Methods ◽  
New Research

Analysing and mining image data to derive potentially useful information is a very challenging task. Image mining concerns the extraction of implicit knowledge, image data relationships, associations between image data and other data or patterns not explicitly stored in the images. Another crucial task is to organize the large image volumes to extract relevant information. In fact, decision support systems are evolving to store and analyse these complex data. This paper presents a survey of the relevant research related to image data processing. We present data warehouse advances that organize large volumes of data linked with images and then, we focus on two techniques largely used in image mining. We present clustering methods applied to image analysis and we introduce the new research direction concerning pattern mining from large collections of images. While considerable advances have been made in image clustering, there is little research dealing with image frequent pattern mining. We shall try to understand why.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siquan Yu ◽  
Jiaxin Liu ◽  
Zhi Han ◽  
Yong Li ◽  
Yandong Tang ◽  
...  
Keyword(s):  
Local Structure ◽  
Image Representation ◽  
Representation Learning ◽  
Classification Problem ◽  
Image Clustering ◽  
Clustering Methods ◽  
Adaptive Clustering ◽  
Complex Procedure ◽  
Convolutional Autoencoder ◽  
Image Representations

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.

scholarly journals An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

2021 ◽  
Vol 13 (13) ◽  
pp. 2501
Author(s):  
Maryam Rahimzad ◽  
Saeid Homayouni ◽  
Amin Alizadeh Naeini ◽  
Saeed Nadi
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Vegetation Index ◽  
Feature Learning ◽  
Optical Data ◽  
Image Clustering ◽  
Surface Model ◽  
Discrimination Power ◽  
Urban Image ◽  
Convolutional Autoencoder

High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Boosted Convolutional AutoEncoder (BCAE) method based on feature learning for efficient urban image clustering. The proposed method was applied to multi-sensor remote-sensing images through a multistep workflow. The optical data were first preprocessed by applying a Minimum Noise Fraction (MNF) transformation. Then, these MNF features, in addition to the normalized Digital Surface Model (nDSM) and vegetation indexes such as Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG(2)), were used as the inputs of the BCAE model. Next, our proposed convolutional autoencoder was trained to automatically encode upgraded features and boost the hand-crafted features for producing more clustering-friendly ones. Then, we employed the Mini Batch K-Means algorithm to cluster deep features. Finally, the comparative feature sets were manually designed in three modes to prove the efficiency of the proposed method in extracting compelling features. Experiments on three datasets show the efficiency of BCAE for feature learning. According to the experimental results, by applying the proposed method, the ultimate features become more suitable for clustering, and spatial correlation among the pixels in the feature learning process is also considered.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals Clustering electricity market participants via FRM models

2020 ◽  
pp. 1-12
Author(s):  
Ayla Gülcü ◽  
Sedrettin Çalişkan
Keyword(s):  
Electricity Market ◽  
Distance Metrics ◽  
Agglomerative Clustering ◽  
Timely Manner ◽  
Short Term ◽  
Clustering Techniques ◽  
Market Participants ◽  
Cosine Distance ◽  
Collateral Mechanism

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

Sign in / Sign up

Export Citation Format

Share Document