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In this study, the similarity between different art movements is investigated. For this purpose, five different art movements are selected and thirty paintings are determined from different painters. By using these paintings, the similarity between paintings inside art movements and from other modern art movements are shown and classified by using mathematical methods. Computational methods are used...
Personal emotions accompany us in our daily life, affecting our learning and work, therefore it is necessary to obtain better understanding of human behavior through emotional assessment. This paper proposes a method for recognizing emotions electroencephalography(EEG) based on relevance vector machine(RVM). Emotional states of two types as positive and negative were selected from a standard database...
Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and...
Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike detection, and a boosted cascade, multiclass kernel...
Currently, huge sizes of indeterminate data are effortlessly collected or created at a very high pace in numerous real-life applications. Classifying this indefinite big data, is computationally intensive as large amount of data is related with existential probability of undefined or undetermined values of raw data. In this study, we propose a data mining approach for the classification of big dataset...
The electrocardiogram (ECG) is becoming a promising technology for biometric human identification. Usually ECG is used for health measurements and this is useful for biometric applications to state that the subject under analysis is live. But an individual identification shouldn't require a classical ECG clinical analysis where several contacts are applied to the person to be identified. In literature,...
This papers deals with supervised texture classification. The extracted features are the image second and third order moments. The number of possible moment lags for 2-D signals increases rapidly with the order of the moment even for small lag neighbourhoods. The paper focuses on the selection of moment lags that optimise classification performance. Lag selection also serves another purpose: it waives...
With recent advances in signal processing and biomedical instrumentation, EEG1 signals can be used as a new communication channel between human and computers. Implementation of this channel is possible by recording and analyzing brain waves. Such a system translates human thoughts for a computer thus it is called a “Brain Computer Interface” or BCI In this paper, a new feature vector for each EEG...
For on-line classification of user states such as emotions or stress levels, we present a new, generic, and efficient physiological feature set. In contrast to common approaches using features specifically tailored to each physiological signal, we break up feature extraction into a simple, signal-specific pre-processing step, and the calculation of a comprehensive set of signal-independent features...
The use of feature vectors obtained by concatenation of different features for text independent speaker identification from clean and telephone speech is studied. The composite feature vectors are examined with GMM and VQ models used to classify speakers. Linear discriminant analysis (LDA), a statistical tool designed to select a reduced set of features for best classification, is applied to enhance...
An experimental analysis of two-dimensional (2D) shape classification method based on moment invariants is presented. Various types of translation, scale and rotation invariants are used to construct feature vectors for classification. The performance is evaluated using five different objects picked up from real scenes with a TV camera. Silhouettes and contours are extracted from nonoccluded 2D objects...
The script ‘Devanagari’ is used in many Indian languages. Hindi language is also under Devanagari script. In this paper recognition of Hindi characters is done by using a three step procedure. First step is preprocessing, in which binarization of the image and separations of characters are performed. Each Hindi word has a horizontal bar on the top of word. That bar is also removed in preprocessing...
Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents:...
the goal of this paper is a new feature selection method for segmentation of very high resolution satellite images. We propose a reasonable number of feature types based on spatial and spectral features containing 1storder and 2nd order statistics, Gabor filter and two spectral indices. Having 227 generated features for each pixel, the appropriate and essential features are selected by three steps...
With the widespread use of Internet, the possibilities of exposing confidential data to invaders or attackers increases. Intrusion Detection System (IDS) is used for detecting various intrusions in network environment and to prevent data from malicious attackers. In this paper, a combined algorithm based on Principal Component Analysis (PCA) and Core Vector Machine (CVM), which is an extremely fast...
Important task in image database is to organize images into appropriate category using different features of images. Image classification is studied for many years. There are various techniques proposed to increase the accuracy of classification. In this paper a novel data mining based approach is proposed for content based image classification. Feature extraction and classification algorithms are...
The personalized music recommender supports the user-favorite songs stored in a huge music database. In order to predict only user-favorite songs, managing user preferences information and genre classification are necessary. In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. We applied...
Automated understanding of human facial expression is an active and concerning research topic. It is expected that in near future full-fledged understanding of human facial expression will enable machines to behave more intelligently. In this paper we proposed a system for automatic facial expression recognition. A consistent combination of Self-Organizing Map (SOM), Learning Vector Quantization (LVQ)...
In order to solve the small sample problems and the linear inseparable problems caused by some nonlinear factors, this paper proposed a method to generate multiple virtual samples similar to the original images by its class, then all virtual samples were combined as a new database for training. The method not only helps to increase more samples, but strengthens the reliance of virtual samples on the...
Despite much success has been achieved, object tracking still remains a challenging research field in computer vision, due to many factors and difficulties such as occlusion, illumination, rotation, pose variance, and intensively motion. To handle them, many classical invariant features, object appearance models, and well-designed but complex tracking frameworks have been proposed. However, they seldom...
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