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Cyberbullying is an activity of sending threatening messages to insult person. To prevent cyber victimization from the activity is challenging. This paper enhanced the Naïve Bayes classifier for extracting the words and examining loaded pattern clustering. The algorithm included two main methods: (1) creating partitions by iteratively relocating from entire datasets into clusters using k-mean clustering...
Power metering automation terminals communicate to main station by mobile cellular network. Hence the terminals occur communication and internal faults in normal case and part of faults have apparent statistical features in data flow term. Based on the power metering automation terminals' historical communication flows records, this dissertation studies on the method of preprocessing and multiply...
This paper proposes a new mesh simplification algorithm which makes effort in reducing the approximation error and improving the mesh regularity of simplified mesh at the same time. In previous mesh simplification researches, algorithms generally focused on the appearance error between the simplified mesh and the original mesh. However, a so-called high quality simplified mesh must have low approximation...
Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards...
This paper presents an unsupervised method to simultaneously identify foreground objects and improve image clustering quality. Given a set of unlabeled images, each image is decomposed into a set of local features and feature weights. First, the unlabeled images are automatically clustered based on feature appearance similarity and geometry similarity. Then, feature weights are recomputed according...
In the fiber image analysis system, correctly segmenting fiber from fiber micrograph is critical for fiber feature extraction and further identification. In this paper, the GVF snake model with the initial contour obtained by contour tracking method based on K-means clustering segmentation is proposed for fiber segmentation. Firstly, the K-means clustering method is used to obtain the initial coarse...
This paper presents a method for mining nonlinear relationships in machine data with the purpose of using such relationships to detect faults, isolate faults and predict wear and maintenance needs. The method is based on the symmetrical uncertainty measure from information theory, hierarchical clustering and self-organizing maps. It is demonstrated on synthetic data sets where it is shown to be able...
Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry...
Although data mining techniques are made tremendous progress, "knowledge-poor" is still a large gap of the current data mining systems. Few researches notice the fact that useful knowledge not only is the final results of an intelligent classification, clustering or prediction algorithm, but also runs through the whole process of data mining in which much potential useful information is...
This paper proposes a method by which the contents of novels may be automatically parsed to extract the key events, characters, locations and significant objects. A technique of clustering relevant to the textual content of novels was derived in order to extract the pertinent elements. The resultant information is presented in the form of a timeline, which can be used to initiate the storyboarding...
With the development of the Broadcasting and Video network, the Monitoring System on Digital Video Broadcasting is becoming more and more important. Image recognition technology is widely applied to detect the degraded video in the television observation system. Mosaic block easily occurs in the TV signals, which will degrade the video quality. The conventional mosaic detection algorithm can't distinguish...
A novel model of fuzzy clustering neural network is discussed, which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model, an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature space. The experimental results obtained demonstrate that the algorithm is feasible and efficient.
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
We propose a method for automatic emotion recognition as part of the FERA 2011 competition. The system extracts pyramid of histogram of gradients (PHOG) and local phase quantisation (LPQ) features for encoding the shape and appearance information. For selecting the key frames, K-means clustering is applied to the normalised shape vectors derived from constraint local model (CLM) based face tracking...
This paper analyzes completely unsupervised clustering of human expressions, gestures, and actions in video. Lacking any supervision, there is nothing except the inherent biases of a given technique to guide grouping of video clips along semantically meaningful partitions. This paper evaluates two contemporary behavior recognition methods, Bag of Features (BOF) and Product Manifolds (PM), for clustering...
Populations of healthy older individuals are often highly heterogeneous, as prevalence of various underlying pathologies increases with age. Finding coherent groups of normal older adults may allow to identify subpopulations that are at risk of developing Alzheimer's disease (AD). In this paper, we propose an approach that utilizes longitudinal magnetic resonance imaging (MRI) data to obtain natural...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
We present a systematic study of the effect of size and shape on the spectral response of individual silver and gold nanoparticles. When developing nanoparticles as catalysts, their shape is very important. For a certain volume of material, nanoparticles make the best catalysts when they have a large surface area. It is a challenge to find the shape that has the largest surface area for its volume...
A novel neural network method to predict the spectral signature in the predicted meteorological image is presented here. Back propagation algorithm has been used in this work. Based on computation cost, three different dimensional feature vectors are provided from two consecutive images as input to neural net for training and testing. Various kinds of testing are made depending upon position of predicted...
In recent years, a content-based method such as `bag-of-features' (BoF) is coming to the fore as an object recognition and classification technique. This paper proposes a BoF signature using invariant region descriptor for object retrieval. The region descriptors are extracted from dense sampled regions in the training images. These descriptors are quantized by hierarchical k-means clustering in a...
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