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Curators, art historians, and connoisseurs are often interested in determining the authorship of paintings. Machine learning and image processing techniques can assist in this task by providing non-invasive, automatic, and objective methods. In this work, we study the automatic identification of Vincent van Gogh's paintings using a Convolutional Neural Network that extracts discriminative visual patterns...
Text detection is a difficult task due to the significant diversity of the texts appearing in natural scene images. In this paper, we propose a novel text descriptor, SPP-net, extracted by equipping the Convolutional Neural Network (CNN) with spatial pyramid pooling. We first compute the feature maps from the original text lines without any cropping or warping, and then generate the fixed-size representations...
Automated classification of HEp-2 cell images is crucial for fast and accurate detection of autoimmune diseases. Recent competitions resulted in high classification rates on publicly available datasets. However, performance on low-resolution HEp-2 images typically lagged behind that of high-resolution images due to the blurring and sub-sampling of fine cellular details. Direct interpolation of low-resolution...
In this paper we describe a simple and very fast method of data acquisition, feature extraction and feature space creation for epileptic seizure detection. The scalp electroencephalogram (EEG) dataset [1, 2] collected at the Children's Hospital Boston from 22 pediatric patients having 192 intractable seizures (available as CHB-MIT database) is used to assess this simple approach against existing ones...
The rapid growth of web source has changed language learning behavior. More and more people utilized web sources instead of paper books. However, the problem now is that it is overwhelming to find useful information. In addition, when considering using different words, good example sentences demonstrating nuance among words are extremely helpful but learners can hardly find them as most web dictionaries...
This paper presents Indonesian text emotion detection and evaluates the performances of four different classification methods: Naive Bayes (NB), J48, K-Nearest Neighbor (KNN) and Support Vector Machine-Sequential Minimal Optimization (SVM-SMO). The experiment uses Indonesian text corpus, containing 1000 sentences which consists of six emotion classes: anger, disgust, fear, joy, sadness, and surprise...
Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an...
In this work, the core objective is to implement an automatic and reliable system for the classification of plants into two plant categories named as monocotyledonous and dicotyledonous using the microscopic images of plant stem cross sections. The system can be used for the classification of plants when a large number of new plant species are discovered and it can be applied in plant disease detection...
In the task of hyperspectral image classification, band selection is often adopted to select a subset of informative bands to reduce the computation and storage cost. We propose a supervised band selection method which allows calculation of a discriminative weight for each band. Specifically, we consider discriminative bands as those that contribute more positive scores to a one-class classifier than...
Recognizing objects in images is a very important research task in the field of computer vision and pattern recognition. We introduce an effective method for object recognition. In order to characterize the appearance of the objects, the SFIT features are extracted from the images. Then, these features are sent to train four classifiers i.e. KNN classifier, Naive Bayes classifier, Decision tree classifier...
We present a novel band weighting strategy that exploits multiple binary support vector machines (SVMs) to maximize interclass spectral distances for multiclass hyperspectral remote image classification. Specifically, we commence by training binary SVMs based on the original training samples. We then balance the bands of training samples by maximizing the modified classification scores for SVMs. This...
To diagnose and classify the dysarthric speech, speech language pathologist (SLP) conducts a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper...
Scene recognition applications on mobile devices receive increasing attentions in recent years. Due to mobile users' real-time requirement, an accurate and efficient scene recognition system is urgent for mobile applications. In this paper, we propose a novel discriminative codeword selection method by using the ensemble extreme learning machine (ELM) algorithm for fast and accurate scene recognition...
Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
Playback attack detection (PAD) is essentially a binary classification task which is used to identify the authentic recordings from the playback recordings. For PAD problem, the difference of the acoustic feature between the authentic and playback recordings mainly comes from the recording channel and the ambient noise. Motivated by the excellent performance of the Gaussian Mixture Model-Universal...
In most document archiving systems, one of the main fields is to identify the category of documents. In most case, determination of the document category in archiving tasks requires the application of classification model, which have had successes in improving documents processing. However, concerns exploding the frequency of use of documents in many office managers have driven increasing interests...
In different applications like Complex document image processing, Advertisement and Intelligent transportation logo recognition is an important issue. Logo Recognition is an essential sub process although there are many approaches to study logos in these fields. In this paper a robust method for recognition of a logo is proposed, which involves K-nearest neighbors distance classifier and Support Vector...
This paper investigates human identification using EEG signals. It has been shown that Electroencephalogram (EEG) can be used as a trait for biometric systems. Previous studies have reported proper channels and features in resting states and mental tasks. However, since EEG signal is sensitive to emotion, the stability of reported features during emotional states is not well verified. Our goal is...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
Digital image processing is still a great demand for research. Research related to digital image processing can be components of color, texture and pattern. This study focuses on the segmentation process of the body pattern of koi. Koi fish is a fish species originating from the country of Japan are much in demand by the people of Indonesia as diverse shades of color and a unique pattern. This study...
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