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To detect image spam effectively, it is necessary to analyze the image content. We do research on the local invariant features of images, and thus propose a novel method: near-duplicate image spam detecting based on CE (cross entropy), in which the SURF (Speeded up Robust Features) is used to extract the local invariant features of each image (spam and ham); then the GMM (Gaussian Mixture Models)...
This paper presents a coarse-to-fine particle segmentation strategy to extract particles from microscopic urinary images within two stages, coarse stage and fine stage. In coarse stage, to locate particles in a wide range of images including the low contrast, the unevenly illuminated, etc, we develop 4-direction variance mapping followed by an adaptive thresholding method. Within this stage, particles...
Fuzzy relational classifier (FRC) is the recently proposed two-step nonlinear classifiers, which effectively integrates the formed clusters and the given classes. However, FRC can not copy with the influence of those irrelevant or redundant features. To effectively filter out those irrelevant features and preserve the internal structure hidden in the given data, in this paper, a simultaneous clustering...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Wardpsilas method, with the latter three being different hierarchical methods. The quality of the clusters created by the algorithms was measured in terms of cluster cohesiveness and semantic cohesiveness, and both quantitative and predicate-based similarity criteria were considered.Two...
The purpose of this article is to present a novel algorithm for ship wake detection in synthetic aperture radar (SAR) images. The main originality of our work is that splitting the image with small window before conventional Radon transform to make the illumination has stronger consistency in each window and adopting clustering algorithm to select real wakes form disturbing lines. Experimental result...
Content based video indexing and retrieval traces back to the elementary video structures, such as a table of contents. Thus, algorithms for video partitioning have become crucial with the unremitting growth in the prevalent digital video technology. This demands for a tool which would break down the video into smaller and manageable units called shots. In this paper, a shot boundary detection technique...
A novel approach for the classification of compressed video data using centroid neural network with Bhattacharyya kernel (CNN(BK)) is proposed in this paper. The proposed classifier is based on centroid neural network (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, since the feature vectors of compressed video data...
We present a method for extracting ground and other planes from a single non rotating laser mounted on a slow moving car used for on-road driving. A laser scan is decomposed into linear clusters. Corresponding clusters from subsequent scans are merged to form planes. The ground plane is identified based on the current vehicle height and the variance in height of the planes. Once these seed planes...
Most of the anomaly based techniques produce vast number of alert messages that include a large percentage of false alarms. One of the widely used technique for anomaly intrusion detection systems (IDS) is cluster analysis. In cluster based IDS, feature vectors generated from network traffic are grouped into clusters as normal or abnormal (raising alert). The main cause for false alert generation...
In this paper, we generalize the notions of centroids (and barycenters) to the broad class of information-theoretic distortion measures called Bregman divergences. Bregman divergences form a rich and versatile family of distances that unifies quadratic Euclidean distances with various well-known statistical entropic measures. Since besides the squared Euclidean distance, Bregman divergences are asymmetric,...
A novel approach for the extraction of rectangular boundaries from aerial image data is proposed and presented in this paper. In this approach, a centroid neural network (CNN) with a metric of line segments is also proposed for connecting low-level linear structures or grouping similar objects. Extracting rectangular boundaries for building rooftops from an edge image without height information of...
In this paper, we describe a new clustering-based classification technique (eVQ-Class), which is able to adapt old clusters and to evolve new ones on-line with new incoming data samples. It extends the conventional learning vector quantization approach, which is a kind of supervised version of original vector quantization, in mainly three points: 1.) it is able toevolve new clusters on demand by comparing...
The method of cluster analysis is usually adopted in spatial data mining research, and this paper studies the theory of the two popular methods of cluster analysis, investigates the interesting ratio of 108 features and attributes in spatial database, uses these two methods respectively to analyze the statistics data and compare the two analysis to get the close results, at last, classifies the current...
The high score pigtail rate satellite remote sensing image may provide the rich data for the urban quakeproof disaster reduction information system's construction. But, because its slight information is specially rich, has brought certain difficulty for the related terrain feature object detection and the extraction. The image division's goal lies in the original image divides some in the space neighboring,...
Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and meaningful clusters from such datasets. Additionally,...
With the increase of XML data over the Internet, managing and analyzing huge amount of XML documents has played an important role for information management. This paper addresses the problem of clustering XML documents. Borrowing the idea of semi-clustering, it proposes a robust clustering method through a combination of single partitional and hierarchical clustering algorithms, which can eliminate...
With the development of image retrieval system, the organizing and managing of image database effectively have been a key technology of users' retrieving. This paper firstly makes use of improved ant colony clustering algorithm in emotional clustering which based on image feature. This algorithm may identify who is the first ant by calculating the Euclidean distance, simulate ants' action of picking...
In this paper, we introduce a clustering algorithm for intrusion detection based on WaveCluster algorithm and an entropy-based characteristics screening algorithm. WaveCluster algorithm has a low time complexity when the data are low-dimensional, but on the contrary, the actual network data are high-dimensional. So we reduce the dimension of the network data using characteristics screening before...
In the problem of face clustering with multi-views, the similarity between faces of different persons with similar pose is usually greater than the similarity between multi-view faces of the same person. This may exert a tremendous impact on the clustering result that sent back to the user. To solve this problem, we should do pose clustering first and then within each dasiapose grouppsila, clustering...
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