A False Acceptance Error Controlling Method for Hyperspherical Classifiers

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

OSLNet:Deep Small-Sample Classification with an Orthogonal Softmax Layer

A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data. To mitigate overfitting in small-sample classification, learning more discriminative features from small-sample data is becoming a new trend. To this end, this paper aims to find a subspace of neural networks that can facilitate a large decision margin. Specifically, we propose the Orthogonal Softmax Layer (OSL), which makes the weight vectors in the classification layer remain orthogonal during both the training and test processes. The Rademacher complexity of a network using the OSL is only $\frac{1}{K}$, where $K$ is the number of classes, of that of a network using the fully connected classification layer, leading to a tighter generalization error bound. Experimental results demonstrate that the proposed OSL has better performance than the methods used for comparison on four small-sample benchmark datasets, as well as its applicability to large-sample datasets. Codes are available at: https://github.com/dongliangchang/OSLNet.Comment: TIP 2020. Code available at https://github.com/dongliangchang/OSLNe

SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations

Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists and continuing to optimize ELBO after this value will not improve inference accuracy. On the basis of the experiment results, we propose SHOT-VAE to address these problems without introducing additional prior knowledge. The SHOT-VAE offers two contributions: (1) A new ELBO approximation named smooth-ELBO that integrates the label predictive loss into ELBO. (2) An approximation based on optimal interpolation that breaks the ELBO value bottleneck by reducing the margin between ELBO and the data likelihood. The SHOT-VAE achieves good performance with a 25.30% error rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on CIFAR-10 with 4k labels.Comment: 12 pages, 6 figures, Accepted for presentation at AAAI202

Open Cross-Domain Visual Search

This paper addresses cross-domain visual search, where visual queries retrieve category samples from a different domain. For example, we may want to sketch an airplane and retrieve photographs of airplanes. Despite considerable progress, the search occurs in a closed setting between two pre-defined domains. In this paper, we make the step towards an open setting where multiple visual domains are available. This notably translates into a search between any pair of domains, from a combination of domains or within multiple domains. We introduce a simple -- yet effective -- approach. We formulate the search as a mapping from every visual domain to a common semantic space, where categories are represented by hyperspherical prototypes. Open cross-domain visual search is then performed by searching in the common semantic space, regardless of which domains are used as source or target. Domains are combined in the common space to search from or within multiple domains simultaneously. A separate training of every domain-specific mapping function enables an efficient scaling to any number of domains without affecting the search performance. We empirically illustrate our capability to perform open cross-domain visual search in three different scenarios. Our approach is competitive with respect to existing closed settings, where we obtain state-of-the-art results on several benchmarks for three sketch-based search tasks.Comment: Accepted at Computer Vision and Image Understanding (CVIU

An automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sens

Abstract: This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the twoRemote Sens. 2009, 1 244 unsupervised SOM networks. Additionally, the classification performance of the refine

HMSN : Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes

Publisher PD

Model and Training Method of the Resilient Image Classifier Considering Faults, Concept Drift, and Adversarial Attacks

Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations