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Text classification is an important research direction of text mining and the research of Chinese text automatic classification is also becoming a research focus of intelligent classification. Against the particularity of the Chinese text classification, this paper presents a three-dimensional vector space model on the basis of the vector space model to improve the accuracy and efficiency of text...
Natural language text describes the nature of people's internal representation of space. It is investigated that 80% of unstructured text has location expressions e.g. place names and spatial relations. In the past few years, text has become a most important geospatial data resource as well as survey, map, satellite images and GPS. The most previous research focused on the recognition of place names...
This paper introduces a system that automatically classifies image pairs based on the type of registration required to align them. The system uses support vector machines to classify between panoramas, high-dynamic-range images, focal stacks, super-resolution, and unrelated image pairs. A feature vector was developed to describe the images, and 1100 pairs were used to train and test the system with...
Using the Mallat fast algorithm with sym5 wavelet, the pulse waves of 20 heroin druggers and 20 healthy normal subjects are decomposed into two levels. The squared distances from the third and tenth scale coefficients in the second-level decomposition of every pulse wave to the global mean value are used to form a feature vector. The extracted feature vectors have good separable characteristics in...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
The mechanism of aircraft skin corrosion is studied and a corrosion detection algorithm based on wavelet analysis is proposed. First of all, the magneto-optic image is decomposed by the wavelet analysis. In the next place a feature vector is assigned whose components represent energy in each sub-image. Lastly, a 1-nearest neighbor method classifier is applied to classify the proceeding feature vector...
In this paper an improvement of wavelet based methods for detection and classification of power quality disturbances is presented. In the feature extraction process wavelet analysis is also used as in the comparing methods. However, the feature vector is extended with three other coefficients in order to improve the accuracy of the algorithm. In order to evaluate the proposed method, large number...
In this paper, we propose a texture feature-based language identification using wavelet-domain BDIP (block difference of inverse probabilities), BVLC (block variance of local correlation coefficients), and NRMA (normalized magnitude) features. The proposed method includes three special operations of NRMA, Donoho's soft-thresholding, and variance thresholding. In the proposed method, wavelet subbands...
We propose an automatic moment-based image recognition technique in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering...
Language Identification is an important issue in today's multilingual world. In this paper we have analyzed Fuzzy-SVM technique for identification of romanized plaintexts of five Indian regional languages namely Hindi, Bangla, Manipuri, Urdu and Kashmiri. Distinguishing features/characteristics have been extracted from romanized plaintexts of each of these five languages and represented suitably through...
Many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line...
Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the ??Bag of Word?? BOW of the documents with term weighting phenomena. Documents representing through this model has some limitations that is, ignoring term dependencies, structure and ordering of the terms in documents...
This paper presents a method for recognizing Bengali printed characters by using view-based and layer-based approaches. Two different view-based approaches, the top-bottom and the left-right have been used. The layer-based approach is also considered here. No thinning or segmentation is required. The individual character is taken as a whole image. The characteristic points are extracted from the views...
A novel method of crowd estimation is proposed in this paper: Firstly, surveillance image is divided into bit planes by OSTU algorithm, the pixel ratio of foreground to background and complexity of bit planes are taken as feature vectors of crowd estimation. The degree of crowd density of the scene is classified into several grades, BP neural network is used for training and then the classification...
We present a novel feature extraction framework, Neighboring Image Patches Embedding (NIPE), for robust and efficient background modeling. We divide image into patches and represent each image patch as a NIPE vector. Then, the background model of each image patch is constructed as a group of weighted adaptive NIPE vectors. The NIPE feature vector, whose components are similarities between current...
Automatic semantic annotation of video events has received a large attention from the scientific community in the latest years. Events can be defined by spatio-temporal relations and properties of objects and entities, which change over time; some events can be described by a set of patterns. Despite this application of dynamic graphical modeling, the performance for event modeling and detection continues...
EEG-based emotion recognition is a relatively new research field in the human computer interaction area and its aim is the implementation of new algorithms that would identify and recognize emotions from EEG (electroencephalogram) signals. Towards that, a novel method is presented in this paper that employs an optimized hybrid filter, using empirical mode decomposition (EMD) and genetic algorithms...
Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications...
Support vector machine (SVM) is a machine learning technique widely applied in classification problems. SVM are based on the Vapnik's Statistical Learning Theory, and successively extended by a number of researchers. On the order hand, the electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. In...
Web classification is considered to be an important and challenging task, it has extracted more and more research work in recent years. Due to domain diversity and complexity, there remain many problems not solved. This work is focus on teaching Web page classification and a novel two-level classification model is proposed. Its processing including two steps: at first, the model employ global feature...
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