A Survey of Outlier Detection Methodologies and Their Applications

Niu, Zhixian; Shu-ping, Shi; Sun, Junying; He, Xiaoli

doi:10.1007/978-3-642-23881-9_50

Cited by 50 publications

(23 citation statements)

References 7 publications

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“…In a statistical analysis, an outlier [19][20][21][22][23][24] is an observation which deviates significantly from other observations [19]. The deviation makes the observation appear to be generated by a different mechanism.…”

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

“…Outlier, in the conventional meaning, is a term used to define any type of anomaly [19]. The term "detection of outlier" [19][20][21][22][23][24] must be used when it is not necessary to categorize the anomaly. The term "detection of anomaly" [13] must be used only when it is necessary to categorize the anomaly.…”

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

“…The potential applications of computational tools for analyzing remote sensing data taken from environments containing large rivers attract much attention in the scientific literature [1][2][3][4][5][6][7][8][9][10][11][12]. Two of these tools, anomaly detection [13,14] and incongruence [15][16][17][18], are important for studies to find and categorize non-conforming patterns present in images, such as outliers [19][20][21][22][23][24]. For example, Qi Liu et al in [25] presented a framework to detect outliers and anomalous events [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54].…”

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confidence: 99%

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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“…The multivariate normal distribution model is built V. PERFORMANCE EVALUATION The model is tested initially on cross validation data [26]. By using the mean and covariance matrix, calculated from training data, probability density function for the cross validation data P val is calculated with the help of the expression given in (16). Then, we quantized P val by a step size defined as;…”

Section: A Anomaly Detection Algorithm Developmentmentioning

confidence: 99%

Feature extraction in X-ray images for hazelnuts classification

Khosa

Pasero

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In the food industry, the importance of automatic detection and selection of raw food ingredients is increasing. In this paper, a method for real time automatic detection, segmentation and classification of hazelnuts using x-ray images is presented. Automatic extraction of independent nut images is made using image processing techniques. To extract meaningful features, moment invariants and texture properties are calculated on global level as well as from co-occurrence matrices. Principal component analysis is applied on features to achieve orthogonality in addition to dimensionality reduction. An anomaly detection algorithm is used for classification. Multivariate Gaussian distributions are calculated for model estimation using training data. Results are calculated on test data by using the threshold value obtained from best validation outcome. The classifier showed 98.6% correct classification rate for negative examples with 0% false negative rate.

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A Method of Peculiarity Factor Approximation Using Vector Quantization Model

Saitoh

Ishizu

2016

Elect Comm in Japan

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SUMMARY The purpose of this research is to reduce the computational complexity of the peculiarity factor (PF). Recently, PF has been adopted as the index for anomaly data detection, and it is widely used in various mining scenes. The fact that PF has become a powerful mining tool is positive because its calculation method is extremely simple and the results of the calculation are easy to understand visually. One of the most important problems for using PF for large‐scale data is the rapidly increasing computational complexity required when the data volume increases. The computational complexity of PF is in the polynomial order because the PF of each data is calculated distantly over all the data. In this study, we propose an approximation methodology for PF for computational reduction and for enhanced robustness using the vector quantization model. Approximate values of PF are calculated by replacing the actual data with the nodes of vector quantization model. By calculating PF based on the vector quantization node vectors, we achieve restraint in the increasing computational complexity.

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A Survey of Outlier Detection Methodologies and Their Applications

Cited by 50 publications

References 7 publications

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Feature extraction in X-ray images for hazelnuts classification

A Method of Peculiarity Factor Approximation Using Vector Quantization Model

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