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Hyperspectral image classification is a challenging classification problem: obtaining complete and representative training sets is costly; pixels can belong to unknown classes; and it is generally an ill-posed problem. The need to achieve high classification accuracy surpasses the need to classify the entire image. To achieve this, we use classification with rejection by providing the classifier an...
Dual-pol HHVV images acquired by TerraSAR-X/TanDEM-X during 4 years, with 3 different incidence angles, over rice fields in Spain are analysed and employed to retrieve the phe-nological state of rice crops in a scale of 3 to 5 possible intervals. A decision tree approach is employed for the retrieval algorithm. Results show that the joint processing of all angles provides a good performance, only...
This paper presents a new method to detect damaged buildings caused by earthquake from high spatial resolution remote sensing image. We found that the probability of multiple gradient orientations is greater in a local area within a damaged building than that in a local area within an intact building. Therefore, a new feature (Local Gradient Orientation Entropy, LGOE) was put forward to determine...
Information fusion is a key research area widely applied to various multimedia analysis tasks such as artificial intelligence, humancomputer interaction, robotics, distributed computing, financial systems and security/surveillance. Feature level fusion has been considered as the most promising fusion method due to the rich information presented at this level. A critical operation of feature level...
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate...
Text Categorization plays an important role in the fields of information retrieval, machine learning, natural language processing, data mining and others. With the development of computer and information technology, there have been many classification algorithms. Each text classification algorithms will get result at differing speeds and efficiency due to the various feature of test text. It has been...
This paper shows an implementation of the Ψ and UML (Updated Maximum Likelihood) methods to incorporate unforced choice paradigms (nAUC) and simulation results for repeatability, efficiency and accuracy. Parametric methods like Ψ and UML promise higher accuracy and efficiency compared to classic and non-parametric methods and support fixed sets of stimuli. Unforced choice paradigms have shown similar...
Random Forest is a well-known ensemble learning method that achieves high recognition accuracies while preserving a fast training procedure. To construct a Random Forest classifier, several decision trees are arranged in a forest while a majority voting leads to the final decision. In order to split each node of a decision tree into two children, several possible variables are randomly selected while...
“Road rage” has been a concern to traffic safety and management authorities. In order to provide an effective detection of angry driving behavior, it is necessary to provide a method for discriminating angry driving from normal. Thirty professional drivers were recruited for conducting on-road experiments in Wuhan, China. The drivers were required to finish the experiment within 110 minutes and their...
Identifying the mentor and the mentee in the online community is very difficult because of the hidden or lacking personal characteristics but it is very important for the organization. The new members in the organization probably will not only require the knowledge explicitly from their colleagues in the same department, but also the implicit knowledge from online community. The mentor and the mentee...
Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it divides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes...
In this paper, a novel cultural event classification algorithm based on convolutional neural networks is proposed. The proposed method firstly extracts regions that contain meaningful information. Then, convolutional neural networks are trained to classify the extracted regions. The final classification of a scene is performed by combining the classification results of each extracted region of the...
Applications that deal with time-series data often require evaluating complex statistics for which each time series is essentially one data point. When only a few time series are available, bootstrap methods are used to generate additional samples that can be used to evaluate empirically the statistic of interest. In this work a novel bootstrap method is proposed, which is shown to have some asymptotic...
In this paper, a statistical method has been proposed to identify motor imagery left and right hand movements from electroencephalogram (EEG) signals in the Dual Tree Complex Wavelet Transform (DTCWT) domain. The total experiment is carried out with the publicly available benchmark BCI-competition 2003 Graz motor imagery dataset. First, the EEG signals are decomposed into several bands of real and...
We present a novel audiovisual emotion recognition solution using multimodal information fusion based on entropy estimation. Considering the limitations of existing methods, we propose a new dual-level fusion framework which consists of feature level fusion module based on kernel entropy component analysis and score level fusion module based on maximum correntropy criterion. In our system, audio and...
Human brain is still known to be the ultimate and the best machine in its ability for decision making. Researchers are in a constant process of understanding and duplicating the functions of the brain; though these efforts are still in infancy. In this present study, we are trying to analyze the processing of different perceptual tasks given to alcoholic and control subjects. The aim is to correlate...
For a significant number of questions at Stack Overflow, none of the posted answers were accepted as solutions. Acceptance of an answer indicates that the answer actually solves the discussed problem in the question, and the question is answered sufficiently. In this paper, we investigate 3,956 such unresolved questions using an exploratory study where we analyze four important aspects of those questions,...
The main aim of this paper is an improvement of the famous Scale Invariant Feature Transform (SIFT) algorithm used in place categorization. Masking approach to reduce the computational complexity of SIFT have been proposed. Tradeoff between key points and processing time on feature extraction has been used. Selected parameters used in the experiment demonstrated that the computational cost of SIFT...
When enterprises outsource maintenance of IT systems to service providers, thorough knowledge acquisition is critical to the success of the engagement. Program comprehension contributes significantly to acquiring knowledge of the IT systems. It is a common practice to execute test scripts to identify critical scenarios in the system and then trace these as flows in the programs. Instead of executing...
Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective...
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