One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

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Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

IEICE Transactions on Information and Systems ◽

10.1587/transinf.2019edl8178 ◽

2020 ◽

Vol E103.D (2) ◽

pp. 450-453

Author(s):

Guizhong ZHANG ◽

Baoxian WANG ◽

Zhaobo YAN ◽

Yiqiang LI ◽

Huaizhi YANG

Keyword(s):

Sparse Representation ◽

Steel Structure ◽

Decision Fusion ◽

One Class Classification

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OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽

10.3390/s21144805 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4805

Author(s):

Saad Abbasi ◽

Mahmoud Famouri ◽

Mohammad Javad Shafiee ◽

Alexander Wong

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Network Architectures ◽

Design Exploration ◽

Convolutional Autoencoder ◽

Acoustic Anomaly ◽

Human Operators ◽

Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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

Sustainability ◽

10.3390/su13095108 ◽

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.

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GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

Electronics ◽

10.3390/electronics10111269 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1269

Author(s):

Jiabin Luo ◽

Wentai Lei ◽

Feifei Hou ◽

Chenghao Wang ◽

Qiang Ren ◽

...

Keyword(s):

Image Denoising ◽

Data Augmentation ◽

Noise Suppression ◽

Random Noise ◽

Similarity Index ◽

Structural Similarity ◽

Training Dataset ◽

Generative Adversarial Network ◽

Multi Scale ◽

Convolutional Autoencoder

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

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GAN-Based One-Class Classification for Remote-Sensing Image Change Detection

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3066435 ◽

2021 ◽

pp. 1-5

Author(s):

Ping Jian ◽

Keming Chen ◽

Wei Cheng

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Remote Sensing Image ◽

Image Change Detection ◽

One Class Classification

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Meta Learning for Few-Shot One-Class Classification

AI ◽

10.3390/ai2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 195-208

Author(s):

Gabriel Dahia ◽

Maurício Pamplona Segundo

Keyword(s):

Feature Representation ◽

Support Vector ◽

Support Vector Data Description ◽

Target Class ◽

Feature Representations ◽

Shot Classification ◽

Training Stage ◽

Comparable Performance ◽

Meta Learning ◽

One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

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A New Upper Bound for Sampling Numbers

Foundations of Computational Mathematics ◽

10.1007/s10208-021-09504-0 ◽

2021 ◽

Author(s):

Nicolas Nagel ◽

Martin Schäfer ◽

Tino Ullrich

Keyword(s):

Upper Bound ◽

Reproducing Kernel ◽

Reproducing Kernel Hilbert Space ◽

Sampling Procedure ◽

Compact Embedding ◽

Recovery Method ◽

Grid Sampling ◽

Integrable Functions ◽

Random Draw ◽

Square Integrable Functions

AbstractWe provide a new upper bound for sampling numbers $$(g_n)_{n\in \mathbb {N}}$$ ( g n ) n ∈ N associated with the compact embedding of a separable reproducing kernel Hilbert space into the space of square integrable functions. There are universal constants $$C,c>0$$ C , c > 0 (which are specified in the paper) such that $$\begin{aligned} g^2_n \le \frac{C\log (n)}{n}\sum \limits _{k\ge \lfloor cn \rfloor } \sigma _k^2,\quad n\ge 2, \end{aligned}$$ g n 2 ≤ C log ( n ) n ∑ k ≥ ⌊ c n ⌋ σ k 2 , n ≥ 2 , where $$(\sigma _k)_{k\in \mathbb {N}}$$ ( σ k ) k ∈ N is the sequence of singular numbers (approximation numbers) of the Hilbert–Schmidt embedding $$\mathrm {Id}:H(K) \rightarrow L_2(D,\varrho _D)$$ Id : H ( K ) → L 2 ( D , ϱ D ) . The algorithm which realizes the bound is a least squares algorithm based on a specific set of sampling nodes. These are constructed out of a random draw in combination with a down-sampling procedure coming from the celebrated proof of Weaver’s conjecture, which was shown to be equivalent to the Kadison–Singer problem. Our result is non-constructive since we only show the existence of a linear sampling operator realizing the above bound. The general result can for instance be applied to the well-known situation of $$H^s_{\text {mix}}(\mathbb {T}^d)$$ H mix s ( T d ) in $$L_2(\mathbb {T}^d)$$ L 2 ( T d ) with $$s>1/2$$ s > 1 / 2 . We obtain the asymptotic bound $$\begin{aligned} g_n \le C_{s,d}n^{-s}\log (n)^{(d-1)s+1/2}, \end{aligned}$$ g n ≤ C s , d n - s log ( n ) ( d - 1 ) s + 1 / 2 , which improves on very recent results by shortening the gap between upper and lower bound to $$\sqrt{\log (n)}$$ log ( n ) . The result implies that for dimensions $$d>2$$ d > 2 any sparse grid sampling recovery method does not perform asymptotically optimal.

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