Deep Self-Evolution Clustering (English)

In: IEEE Transactions on Pattern Analysis and Machine Intelligence   ;  42 ,  4  ;  809-823  ;  2020

How to get this document?

Download
Commercial Copyright fee: €27.65 Basic fee: €4.00 Total price: €31.65
Academic Copyright fee: €27.65 Basic fee: €2.00 Total price: €29.65

Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar. Specifically, similarities between pairwise patterns are defined by the dot product between indicator features which are generated by a deep neural network (DNN). To learn informative representations for clustering, clustering constraints are imposed on the indicator features to represent specific concepts with specific representations. Since the ground-truth similarities are unavailable in clustering, an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise patterns and to train the DNN alternately. Consequently, the indicator features tend to be one-hot vectors and the patterns can be clustered by locating the largest response of the learned indicator features. Extensive experiments strongly evidence that DSEC outperforms current models on twelve popular image, text and audio datasets consistently.

Table of contents – Volume 42, Issue 4

Show all volumes and issues

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

765
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Kuehne, Hilde / Richard, Alexander / Gall, Juergen | 2020
780
Automated Video Face Labelling for Films and TV Material
Parkhi, Omkar M. / Rahtu, Esa / Cao, Qiong / Zisserman, Andrew | 2020
793
Baselines Extraction from Curved Document Images via Slope Fields Recovery
Meng, Gaofeng / Pan, Chunhong / Xiang, Shiming / Wu, Ying | 2020
809
Deep Self-Evolution Clustering
Chang, Jianlong / Meng, Gaofeng / Wang, Lingfeng / Xiang, Shiming / Pan, Chunhong | 2020
824
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Malkov, Yu A. / Yashunin, D. A. | 2020
837
Extracting Geometric Structures in Images with Delaunay Point Processes
Favreau, Jean-Dominique / Lafarge, Florent / Bousseau, Adrien / Auvolat, Alex | 2020
851
Group Maximum Differentiation Competition: Model Comparison with Few Samples
Ma, Kede / Duanmu, Zhengfang / Wang, Zhou / Wu, Qingbo / Liu, Wentao / Yong, Hongwei / Li, Hongliang / Zhang, Lei | 2020
865
Hierarchical Bayesian Inverse Lighting of Portraits with a Virtual Light Stage
Shahlaei, Davoud / Blanz, Volker | 2020
880
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
Lian, Chunfeng / Liu, Mingxia / Zhang, Jun / Shen, Dinggang | 2020
894
On Detection of Faint Edges in Noisy Images
Ofir, Nati / Galun, Meirav / Alpert, Sharon / Brandt, Achi / Nadler, Boaz / Basri, Ronen | 2020
909
Perspective-Adaptive Convolutions for Scene Parsing
Zhang, Rui / Tang, Sheng / Zhang, Yongdong / Li, Jintao / Yan, Shuicheng | 2020
925
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
Lu, Canyi / Feng, Jiashi / Chen, Yudong / Liu, Wei / Lin, Zhouchen / Yan, Shuicheng | 2020
939
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning
Gao, Jin / Wang, Qiang / Xing, Junliang / Ling, Haibin / Hu, Weiming / Maybank, Stephen | 2020
956
Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding
Yu, Hong-Xing / Wu, Ancong / Zheng, Wei-Shi | 2020
974
Visibility Graphs for Image Processing
Iacovacci, Jacopo / Lacasa, Lucas | 2020
988
Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets
Clough, James R. / Balfour, Daniel R. / Cruz, Gastao / Marsden, Paul K. / Prieto, Claudia / Reader, Andrew J. / King, Andrew P. | 2020
998
Denoising Autoencoders for Overgeneralization in Neural Networks
Spigler, Giacomo | 2020
1005
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Veksler, Olga | 2020
1013
Learning Raw Image Reconstruction-Aware Deep Image Compressors
Punnappurath, Abhijith / Brown, Michael S. | 2020
1020
2020 COMPSAC CFP
| 2020
C1
Table of Contents
| 2020
C2
Cover
| 2020