Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features

Zrira, Nabila; Khan, Haris Ahmad; Bouyakhf, El Houssine

doi:10.1007/s12559-017-9534-9

Cited by 11 publications

(2 citation statements)

References 41 publications

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“…[17] represents images as a set of regions by exploiting local deep representations for indoor image place categorization. [18] trains a discriminative deep belief network (DDBN) classifier on the GIST features of the image. Driving scene categorization is conducted in [19] by combining the features representational capabilities of CNN with the VLAD encoding scheme, and [7] trains an end-toend CNN for this task.…”

Section: A Scene Categorization Methodsmentioning

confidence: 99%

An Image-based Approach of Task-driven Driving Scene Categorization

Hu,

Fan,

Gao

et al. 2021

Preprint

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Categorizing driving scenes via visual perception is a key technology for safe driving and the downstream tasks of autonomous vehicles. Traditional methods infer scene category by detecting scene-related objects or using a classifier that is trained on large datasets of fine-labeled scene images. Whereas at cluttered dynamic scenes such as campus or park, human activities are not strongly confined by rules, and the functional attributes of places are not strongly correlated with objects. So how to define, model and infer scene categories is crucial to make the technique really helpful in assisting a robot to pass through the scene. This paper proposes a method of task-driven driving scene categorization using weakly supervised data. Given a front-view video of a driving scene, a set of anchor points is marked by following the decision making of a human driver, where an anchor point is not a semantic label but an indicator meaning the semantic attribute of the scene is different from that of the previous one. A measure is learned to discriminate the scenes of different semantic attributes via contrastive learning, and a driving scene profiling and categorization method is developed based on that measure. Experiments are conducted on a front-view video that is recorded when a vehicle passed through the cluttered dynamic campus of Peking University. The scenes are categorized into straight road, turn road and alerting traffic. The results of semantic scene similarity learning and driving scene categorization are extensively studied, and positive result of scene categorization is 97.17 % on the learning video and 85.44% on the video of new scenes.

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Section: A Scene Categorization Methodsmentioning

confidence: 99%

An Image-based Approach of Task-driven Driving Scene Categorization

Hu,

Fan,

Gao

et al. 2021

Preprint

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“…Although deep learning is widely used in the fields of pattern recognition, speech recognition, and natural language processing, it has few promising applications in the field of cyber intrusion prevention. The application of DDBN to cyber intrusion protection is the novelty of this study [44]- [46].…”

Section: Introductionmentioning

confidence: 99%

Cyber Intrusion Prevention for Large-Scale Semi-Supervised Deep Learning Based on Local and Non-Local Regularization

Xian

2020

IEEE Access

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The cyber intrusion prevention model represents a new means of cyber protection with intelligent defense capability. It can not only detect intrusion behavior but also respond to such behavior in a timely manner. This study applies deep learning theory and semi-supervised clustering to cyber intrusion prevention technology. Deep learning based on deep structures represents the current development trend of neural networks. Semi-supervised learning uses a large amount of unlabeled cyber traffic data and a small amount of labeled cyber traffic data to achieve cyber intrusion prevention with a low recognition error rate. Discriminative deep belief network (DDBN)-based cyber defense technology has emerged as a research hotspot in the field of cyber intrusion prevention owing to its low error rate. This paper proposes a cyber intrusion prevention technology using DDBN for large-scale semi-supervised deep learning based on local and non-local regularization to overcome the problem of high classification error rates of the cyber intrusion prevention model. Through comparisons with the cyber intrusion prevention results of the Hopfield, support vector machine (SVM), generative adversarial network (GAN), and deep belief network-random forest (DBN-RFS) classifiers, the proposed DDBN model is shown to have the lowest error rate. Thus, the proposed approach can improve the performance of the cyber intrusion prevention system. The training and testing error rates of the exponent loss function with local and non-local regularization (exponent with LNR) are lower than those of the exponent, square, and hinge loss functions. The experimental results show that the running time decreases as the number of hidden layers increases, especially with 6144 and 4096 hidden layer nodes. INDEX TERMS Cyber security, discriminative deep belief networks, intrusion prevention, local and non-local regularization, semi-supervised deep learning.

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