A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

Souza, Renato William R. de; Oliveira, Joao Vitor Chaves de; Passos, Leandro Aparecido; Ding, Weiping; Papa, João Paulo; Albuquerque, Victor Hugo C. de

doi:10.1109/tfuzz.2019.2949771

Cited by 20 publications

(7 citation statements)

References 29 publications

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“…The clustering technique utilized in this research is fuzzy c-means, which is applicable to many surveillance applications such as similar frames clustering or differentiating among different shots or activities. Similar fuzzy logic-based emerging techniques are available in the literature, such as those in [71] for noise filtering, in [72] for features selection of large-scale classification problems, and in [73] for optimum-path forest classification. These approaches can be easily transformed to the surveillance domain for useful video analysis tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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“…Graph theory describe strong architectures capable of modelling complex relationships, with applications ranging from small world design [17] to data classification [18] and oversampling [19]. With the advent of deep learning, GNNs emerged as an elegant solution to extract in-depth dependencies from such intricate relationships.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement

Passos,

Papa,

Del Ser

et al. 2022

Preprint

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This paper proposes a novel multimodal self-supervised architecture for energy-efficient AV speech enhancement by integrating graph neural networks with canonical correlation analysis (CCA-GNN). This builds on a state-ofthe-art CCA-GNN that aims to learn representative embeddings by maximizing the correlation between pairs of augmented views of the same input while decorrelating disconnected features. The key idea of the conventional CCA-GNN involves discarding augmentation-variant information and preserving augmentation-invariant information whilst preventing capturing of redundant information. Our proposed AV CCA-GNN model is designed to deal with the challenging multimodal representation learning context. Specifically, our model improves contextual AV speech processing by maximizing canonical correlation from augmented views of the same channel, as well as canonical correlation from audio and visual embeddings. In addition, we propose a positional encoding of the nodes that considers a prior-frame sequence distance instead of a feature-space representation while computing the node's nearest neighbors. This serves to introduce temporal information in the embeddings through the neighborhood's connectivity. Experiments conducted with the benchmark ChiME3 dataset show that our proposed prior frame-based AV CCA-GNN reinforces better feature learning in the temporal context, leading to more energy-efficient

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“…Fuzzy Optimum-Path Forest (Fuzzy OPF) [35] is a variant of the OPF classifier designed as a pattern recognition technique. Recently, OPF and its variations have been used to design approaches for classifying problems in different areas, including detecting anomalies in wireless networks and obtaining promising results [8,36,37].…”

Section: Introductionmentioning

confidence: 99%

Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

Souza

Medeiros

et al. 2022

Soft Comput

Self Cite

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The detection of intrusions in IoT networks is essential to maintain the availability and integrity of data transmitted and generated by devices connected to these networks. This is primarily when the data originates from critical activities, such as activities in the military, financial, industrial, and health sectors. Machine learning techniques have been adopted to create ways to detect or improve the accuracy of existing models for automatic intrusion detection. However, it is difficult to find in the literature an accurate intrusion detection technique in an IoT environment, as there are different types of attacks that can happen in different ways. Therefore, to solve this problem, this work proposes applying Fuzzy OPF (Optimum-Path Forest) as a new detection algorithm for any threat that escapes the regular traffic of an IoT network. We evaluate our proposed approach by using five different ML algorithms: Linear Discriminant Analysis, Support Vector Machine, Bayes, K-Nearest Neighbors, and Optimum-Path Forest. Experimental results analysis showed that our proposed model outperforms well-known algorithms in the literature regarding the Accuracy, Recall, and F1 metrics.

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A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

Cited by 20 publications

References 29 publications

Fuzzy Logic in Surveillance Big Video Data Analysis

Fuzzy Logic in Surveillance Big Video Data Analysis

Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement

Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

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