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Crowd density estimation is an effective automated video surveillance technique to ensure crowd safety. In spite of various efforts being taken to estimate crowd density, it remains a challenging task. This paper proposes a new texture feature-based approach for the estimation of crowd density where two efficient texture features namely Local Binary Pattern (LBP) and Gabor Filter are used. The LBP...
In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research — "Humans use summary statistics to perceive auditory sequences" we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing...
Timely and accurate traffic classification and application characterization are becoming increasingly important with many applications in wired and wireless networks, e.g., traffic engineering, security monitoring, and quality of service (QoS). In particular, Software Defined Networking (SDN) is a new networking paradigm that has great impact on future IP networks and 5G wireless networks. In SDN...
A fault diagnosis method was proposed based on Semi-supervised manifold learning and Transductive support vector machine (TSVM), to overcome scarcity of labeled training samples. Firstly, wavelet packet decomposition (WPD) was used to decompose vibration signals into several sub-bands. The fault features were extracted from the sub-bands to construct a high-dimensional fault feature set, and the improved...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying...
Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kernels are a priori computed. Moreover, they adopt complex...
In this paper, we explored the development of an anxiety detection (AnD) system using the respiratory signal as its input. Time and frequency domain statistical features derived from breath-to-breath (BB) interval series of respiratory signal is input to a support vector machine (SVM) backend classifier. We used data from normative population, individuals with anxiety disorders and regular meditators...
Essays in different text genres have different ideas and writing method. Prediction the text genres firstly will help get a better accuracy when predicting the success of literary or finding the beautiful words and sentences in the essay. And it will help set a different standard for different text genres when scoring the writing by computer. Words and structure can be effective in discriminating...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Pedestrian detection is considered as an active area of research and the advent of autonomous vehicles for a smarter mobility has spearheaded the research in this field. In this paper, design of a real-time pedestrian detection system for autonomous vehicles is proposed and its performance is evaluated using images from standard datasets as well as realtime video input. The proposed system is designed...
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough deep, which might not extract very discriminant features for classification. In addition, they do not consider strong correlations among different hierarchical layers. Due to the two problems, a hybrid deep residual network is presented...
In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
Temporal sequences of images called Satellite Image Time Series (SITS) allow land cover monitoring and classification by affording a large amount of images. Many approaches attempt to exploit this multi-temporal data in order to extract relevant information such as classification-based techniques. In this paper we compare low and high levels classification-based approaches that aim to reveal the SITS...
Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring...
Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in...
Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and diagnose HCC in its initial stages. The proposed CAD comprises the following stages: image enhancement, liver segmentation, feature extraction and characterization...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
At present, it is a great challenge that solving high-dimension and text sparsity problems in short text classification. To resolve these problems, this paper proposes a method which takes the correlation between lexical items and tags before completing Latent Dirichlet Allocation(LDA) topic model. Meanwhile, this paper adjusts parameters of Support Vector Machine(SVM) to find the optimal values by...
Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM)...
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