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Batik cloth is Indonesia's national heritage. Across the archipelago, there are numerous patterns and motifs of batik, each having its own meaning and cultural significance. In this paper, we present the results of our investigation of various combinations of SIFT features moments used in automatic classification of batik motifs. The classification method used in this paper is the k-Nearest Neighbor...
The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal...
Current smartphones are equipped with various sensors, which can be used for research and data collection purposes. This papers presents an approach to use the gyroscope sensor present in many smartphones to perform gesture recognition. Two phones were strapped onto the subject body. Gyroscope readings were obtained during several gestures. The gyroscope readings were sent to MATLAB using the SensorUDP...
Agarwood, also known as Gaharu in Malaysia, is a fragrant and valuable international commodity harvested from Aquilaria and Gyrinops tree species. The quality of agarwood depends on many factors, such as the quality of its wood resin, smell and origin. Current methods for determining its quality rely on human experts. However, an automated approach would be more suitable for mass production. In this...
Reliability of unattended ground sensors (UGS) to detect and classify different activities (e.g., walking and digging) is often limited by high false alarm rates, possibly due to the lack of robustness of the underlying algorithms in different environmental conditions (e.g., soil types and moisture contents for seismic sensors), inability to model large variations in the signature of a single activity...
In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept — a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ1-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ1-norm penalty, named...
Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large...
This paper provides a novel and unified framework of representation based classification technique. The proposed atomic representation based classification (ARC) framework includes, but not limited to, sparse representation based classification (SRC), low-rank representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant...
We investigated batch and stochastic Manhattan Rule algorithms for training multilayer perceptron classifiers implemented with memristive crossbar circuits. In Manhattan Rule training, the weights are updated only using sign information of classical backpropagation algorithm. The main advantage of Manhattan Rule is its simplicity, which leads to more compact hardware implementation and faster training...
The article presents an application of Adaptive Splitting and Selection (AdaSS) ensemble classifier in a real-life task of designing an efficient clinical decision support system for breast cancer malignancy grading. We approach the problem of cancer detection form a different angle - we already know that a given patient has a malignant type of cancer and we want to asses the level of that malignancy...
In this paper, we propose classifiers based on Tensor Voting (TV) framework for supervised binary and multiclass problems. Traditional classification approaches classify a test sample or point based on its proximity to classes of a training set, where proximity is generally taken as some variant of the Euclidean distance in the original or some transformed higher dimensional space. However, we may...
This paper describes an artificial neural network (ANN) based classification of human gait state. ANN is a well known classifier which is widely applied in many field of applications such as medical, business, computer vision and engineering. This study employs the understanding and knowledge of the human gait analysis. Human gait refers to one's walking pattern. In most cases, gait is used to identify...
The classical front end analysis in speech recognition is a spectral analysis which parameterizes the speech signal into feature vectors. This paper proposes a voice recognition model that is able to automatically classify and recognize a voice signal with background noise. The model uses the concept of spectrogram, pitch period, short time energy, zero crossing rate, mel frequency scale and cepestral...
We propose a new unsupervised method to identify Named Entities (NE) in resource-poor languages. The idea is to transfer the knowledge of NEs from a resource-rich language to a resource-poor one by using a bilingual parallel corpus of this language pair. After extracting all NE pair candidates and filtering these candidates (includes lexical and contextual filters) to obtain a high precision seed...
Classification of conservation tillage practices from hyper-spectral imagery is challenging due to spectral similarity between soils and senescent crop residues. In this paper, a novel classifier using both spectral and spatial information is proposed for hyperspectral image classification. Three steps are included: (1) a feature extraction method using a very simple local averaging filter to produce...
Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show...
A novel multiple classifiers fusion approach based on SFLS (Shortest Feature Line Segment) is proposed in this paper. SFLS is a kind of simple yet effective classification method depending on the shortest feature line. The original form of SFLS's output is just class label. To use SFLS as the member classifier in the multiple classifier system, the form of SFLS's output is modeled using the membership...
Performance robustness of feature extraction with respect to environmental uncertainties is often critical for automated target detection & classification. This paper focuses on performance robustness in the sense that the extracted features are desired to be largely insensitive to environmental uncertainties, while they should be capable of recognizing the effects of small perturbations in the...
Previous findings suggest that temporal coherence between Blood-Oxygen-Level Dependent (BOLD) activation in certain areas is specifically related to the micro-structural organization of fascicles, i.e., the more organized the fibers, the more intense is the communication between areas. This assumption was considered in the analysis of functional and effective connectivity in patients with AD. Support...
The k-nearest neighbor classification method predicts the class label of a query pattern based on its nearest neighbors. So which samples can be selected as the nearest neighbors of the query pattern and how to use these neighbor samples to predict the class label of the query pattern are two key problems in the nearest neighbor based method. Based on the definition of mutual information distance...
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