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There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based formulation: we first rigorously show its equivalence with traditional approaches, and then propose QuickMatch,...
For text clustering task, distinctive text features selection is important due to feature space high dimensionality. It is essential to reduce the feature space dimension to increase accuracy and decrease processing time. In this work, for text clustering task, we introduce a novel hybrid feature selection model. This method measures the term importance based on the correlation coefficient among four...
For low density crowd, the statistical information of pixels and feature points can reflect the change of crowd density. Therefore, pixels and corners are fused in this paper, then, SVR is used to learn the corresponding relationship between feature and the number of people. While PSO is used to optimize the choice of parameters C and gamma in SVR. The experimental results show that the SVR optimized...
This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy function for unsupervised PolSAR image classification...
It seems necessary to detect a broken bike rooted at a station in near realtime as the number of bikes within bikeshare systems has reached more than a million in 2015. Indeed, a bike that cannot be moved is not cost effective in terms of number of trips. This brings frustration to users who were expecting to find a bike at that station without knowing that it is actually defective. We thus propose...
Hyperspectral image clustering is commonly applied for unsupervised classification. However, the clustering results of traditional methods are not sufficient seeing the nature of the image as a data cube with high dimensionality. In addition, the complex relations between spatial neighboring pixels are not considered in traditional methods. In this paper the fuzzy c-means clustering is revisited and...
With the rapid development of uncertain and large-scale datasets, Fuzzy Possibilistic C-means Clustering (FPCM) and Granular Computing (GrC) were introduced together with the aim to solve the feature selection and outlier detection problems. Utilizing the advantages of the FPCM and GrC, an Advanced Fuzzy Possibilistic C-means Clustering based on Granular Computing (GrFPCM) was proposed to select features...
For multi-homed networks, inter-domain traffic engineering (TE) consists in selecting the best path via available transit providers, so that the transmission quality is improved in front of network events, such as congestion and fail-over. In practice, this choice bases on end-to-end (e2e) measurements toward destination networks. These measurements, especially Round-Trip Time (RTT), are expected...
Network intrusion detection systems need to detect abnormal behaviour in network data as soon as possible and with as little user intervention as possible. In this paper, we describe a semi-supervised network anomaly detection system. Our system uses online clustering to summarize the available network data. Clusters are represented using extended cluster features that comprise of not only features...
Figure-Ground Segmentation simply means separating foreground from it's background. It has many applications in day to day life. Object recognition is one of the main applications of it. Extracting foreground from it's background is not an easy task. Various techniques are available for figure-ground segmentation. In this paper, a new approach is proposed to extract foreground from background. A high...
To segment multi-spectral remote sensor images, feature extraction and object classification is an essential step that performs region-based segmentation instead of a pixel-based segmentation. Spectral based segmentation methods like K-Means, Mean-shift segmentation fail to extract optimal regions from multi-spectral images. In high-resolution multi-spectral images, segmentation main aim is to divide...
DNS has been increasingly abused by adversaries for cyber-attacks. Recent research has leveraged DNS failures (i.e. DNS queries that result in a Non-Existent-Domain response from the server) to identify malware activities, especially domain-flux botnets that generate many random domains as a rendezvous technique for command-&-control. Using ISP network traces, we conduct a systematic analysis...
Automatic text detection and extraction systems for natural scene images and videos have gained wide attention due to its immense applications in various fields of information retrieval. Many algorithms have been proposed in literature which addresses the problem of text detection. The color uniformity of text characters is one of the strong features which is used in color based text localisation...
Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. Video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Key frame extraction is a very useful technique to provide a concise access to the video content and is the first step towards efficient browsing and retrieval...
The corpus callosum is one of the most important structures in human brain. Most of the neurological disorders reflect directly or indirectly on the morphological features of Corpus Callosum. The mid-sagittal brain Magnetic Resonance images fully describe the anatomical structure of corpus callosum. Often considered challenging task of segmenting Corpus Callosum from Magnetic Resonance images has...
In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features,...
In text document clustering documents are represented as feature vectors where features can be either words or phrases. Documents can belong to different topics when categorized by humans; however it is noted that obtaining one to one mapping between the features and the topics is almost impossible since the same features can and will be used in documents in different topics. Such common features...
This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver's driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data...
With the development of the Internet, web service generates a large amount of log information, how to mine user preferred browsing paths from web log information is an important research area. Current researches mainly focus on the mining of user preferred browsing paths, however, they do not delve into the personalization of preferred paths and paths lack semantic information. To provide personalized...
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