Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the ...Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.展开更多
Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compres...Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.展开更多
This paper presents a scheme for improving encoding time for fractal image compression. The approachcombines feature extraction with domain classification using a selforganizing neural network. Feature extractionreduc...This paper presents a scheme for improving encoding time for fractal image compression. The approachcombines feature extraction with domain classification using a selforganizing neural network. Feature extractionreduces the dimensionalics of the problem and enables the neural network to be trained on an image separate fromthe test image. The seaorganizing network introduces a neighborhood topology for classytcation, and alsoeliminates the need to specify a prior set of appropriate image classes. The network organizes itself according to thedistribution of the image features observed during the training. The paper presents results showing that thisclassification approach can reduce encoding time by two orders of magnitude while maintaining comparableaccuracy and compression performance.展开更多
We present an approach for generating a sort of fractal graphs by a simpleprobabilistic logic neuron network and show that the graphs can be representedby a set of compressed codings. An algorithm for quickly finding ...We present an approach for generating a sort of fractal graphs by a simpleprobabilistic logic neuron network and show that the graphs can be representedby a set of compressed codings. An algorithm for quickly finding the codings,i.e., recognizing the corresponding graphs, is given. The codings are shown tobe optimal. The results above possibly give us the clue for studying imagecompression and pattern recognition.展开更多
图神经网络是一种强大的学习图数据的模型,通过节点信息嵌入和图卷积运算实现图结构数据的表示。图数据中节点的结构信息和节点的位置信息对获取图特征至关重要,但现有的图神经网络同时捕获位置信息和结构信息的表达能力有限。对此,提...图神经网络是一种强大的学习图数据的模型,通过节点信息嵌入和图卷积运算实现图结构数据的表示。图数据中节点的结构信息和节点的位置信息对获取图特征至关重要,但现有的图神经网络同时捕获位置信息和结构信息的表达能力有限。对此,提出了一种新的图神经网络——融合位置和结构信息的图神经网络(Positional and Structural Information with Graph Neural Networks, PSI-GNN)。PSI-GNN的核心思想在于利用编码器获取节点的位置和结构信息,并将这些信息特征嵌入到网络中。通过在网络中更新和传递这两种信息,PSI-GNN实现了对位置和结构信息的有效融合与利用,为解决上述问题提供了有效的解决方案。同时,为应对不同类型的图学习任务,PSI-GNN给予位置和结构信息以不同的权重来应对不同的下游任务。为了验证PSI-GNN的有效性,在多个基准图数据集上进行了实验。实验结果表明,PSI-GNN在节点级任务上最高提升了约14%,在图级任务上最高提升了约35%,验证了PSI-GNN在同时捕获位置和结构信息方面的有效性。展开更多
文摘Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.
文摘Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.
文摘This paper presents a scheme for improving encoding time for fractal image compression. The approachcombines feature extraction with domain classification using a selforganizing neural network. Feature extractionreduces the dimensionalics of the problem and enables the neural network to be trained on an image separate fromthe test image. The seaorganizing network introduces a neighborhood topology for classytcation, and alsoeliminates the need to specify a prior set of appropriate image classes. The network organizes itself according to thedistribution of the image features observed during the training. The paper presents results showing that thisclassification approach can reduce encoding time by two orders of magnitude while maintaining comparableaccuracy and compression performance.
文摘We present an approach for generating a sort of fractal graphs by a simpleprobabilistic logic neuron network and show that the graphs can be representedby a set of compressed codings. An algorithm for quickly finding the codings,i.e., recognizing the corresponding graphs, is given. The codings are shown tobe optimal. The results above possibly give us the clue for studying imagecompression and pattern recognition.
文摘图神经网络是一种强大的学习图数据的模型,通过节点信息嵌入和图卷积运算实现图结构数据的表示。图数据中节点的结构信息和节点的位置信息对获取图特征至关重要,但现有的图神经网络同时捕获位置信息和结构信息的表达能力有限。对此,提出了一种新的图神经网络——融合位置和结构信息的图神经网络(Positional and Structural Information with Graph Neural Networks, PSI-GNN)。PSI-GNN的核心思想在于利用编码器获取节点的位置和结构信息,并将这些信息特征嵌入到网络中。通过在网络中更新和传递这两种信息,PSI-GNN实现了对位置和结构信息的有效融合与利用,为解决上述问题提供了有效的解决方案。同时,为应对不同类型的图学习任务,PSI-GNN给予位置和结构信息以不同的权重来应对不同的下游任务。为了验证PSI-GNN的有效性,在多个基准图数据集上进行了实验。实验结果表明,PSI-GNN在节点级任务上最高提升了约14%,在图级任务上最高提升了约35%,验证了PSI-GNN在同时捕获位置和结构信息方面的有效性。
