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Lost image areas with different size and arbitrary shape can occur in many scenarios such as error-prone communication, depth-based image rendering or motion compensated wavelet lifting. The goal of image reconstruction is to restore these lost image areas as close to the original as possible. Frequency selective extrapolation is a block-based method for efficiently reconstructing lost areas in images...
Brain Computer Interface (BCI) has become one of the most interesting alternatives to support automatic systems able to interpret brain functions. Recently, the Motor Imagery (MI) paradigm is a widely topic of interest as a tool to develop BCI-based systems. Here, we present a relevant feature extraction methodology, termed MI discrimination using kernel relevance analysis (MIDKRA), to support MI...
Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical...
We have applied Latent Topic Models to facial expression recognition. We showed that the latent topic learned from a topic model is very similar to the Action Units defined by psychologists in the Facial Action Coding Systems (FACS). Furthermore, we noted that the topics thus obtained may be correlated with each other, and we tried to model this by the correlated topic model (CTM). Preliminary results...
In this work, a method is proposed for classification of texture images using a fusion of feature sets. Weighted guided filter based preprocessing technique has been performed using optimized cost function to enhance the discriminative property of different texture images. A hybrid model of normalized symmetrical gray level co-occurrence matrix parameters, histogram of oriented gradients, and Gabor...
In this paper, a novel supervised locality preserving projection method based on differential mode is proposed. We proposed the concept of within-class separation degree and between-class separation degree. The discriminant criterion of difference mode based on within-class separation degree matrix and between-class separation degree matrix is constructed. Thus, the singular problem of within-class...
In character recognition, convolutional neural network (CNN) outperforms most of the other models. However, to guarantee a satisfactory performance, CNNs usually need a great number of samples. Due to the differences between the Chinese and the alphanumeric characters, the most common way to recognize the two classes is to use two independent CNNs respectively. In this paper, to solve the problem...
Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning, and making artificial intelligence and human-computer interaction advance with big strides. We applied deep learning to handwritten character recognition, and explored the two mainstream algorithm of deep learning: the Convolutional Neural Network (CNN) and...
Support Vector Machines (SVM) is a statistical classification approach which has been successfully applied to various types of problems. However, it has remained largely unexplored for Arabic recognition. SVMs are originally designed for binary classification problems. For multi-class problems, several methods used a combination of binary SVMs while some others solved the problem in one step. This...
In this paper, classifying and indexing hierarchical video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors; Mel Frequency Cepstral...
Vectorial compound representation has played an important role in the recent progress in material property prediction based on machine learning methods. However, the material compounds are originally recorded in the material databases as non-vectorial graph units and space groups. The representation of compounds as handmade vectorial representations is challenging and crucial for the successful application...
This paper is an exploration to find a way to get the person attributes in profiles. Considering those attributes exists in large volume of unstructured data, and it is very difficult to gain in a short time. So, we use a method combing the pattern and SVM to extract the person attributes. Firstly, we collect many raw profiles in websites by our configurable crawler. Secondly, we use statistic methods...
It is effective to ensure the credibility of program to monitor the real-time status in the whole process when the program is been executed. Aiming at the problem of high convexity of feature extracting and interaction between false positive and false negative rate in current study, this paper proposed one kind of program behaviour model based on the dependencies. It constituted variable-length system...
In order to accelerate smart grid development and promote distribution automation technology for large area application, aiming at the problems of operational difficulties and poor share about data existing in current C/S structure of distribution automation terminal, a design of distribution automation terminal based on 3 layer B/S schema embedded web server is put forward by researching and analyzing...
Lipreading techniques have shown bright prospects for speech recognition under noisy environments and for hearing-impaired listeners. In this paper, we discuss a feature extraction method based on the homeomorphic manifold analysis for lipreading. Given a set of image sequences, we think there is an underlying low dimensional unified manifold embedded in the visual space, and each image sequence can...
Data centers require many low-level network services to implement high-level applications. Key-Value Store (KVS) is a critical service that associates values with keys and allows machines to share these associations over a network. Mostexisting KVS systems run in software and scale out by running parallel processes on multiple microprocessor cores to increase throughput. In this paper, we take an...
Kernel fuzzy clustering has been applied to data with nonlinear relationships. Two approaches were used: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn't work well with “multiple-density” clusters, Multiple Kernel Fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the...
To the problem that the identify of software and hardware information in the reverse analysis of Network equipment firmware image, a firmware header parsing scheme based on matching features of the field has been proposed. Through the analysis of the firmware header got feature values and build signature database, then use matching fields to identify the characteristics firmware header information,...
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...
In many real-world applications such as image classification, labeled training examples are difficult to obtain while unlabeled examples are readily available. In this context, semi-supervised learning methods take advantage of both labeled and unlabeled examples. In this paper, a greedy graph-based semi-supervised learning (GGSL) approach is proposed for multi-class classification problems. The labels...
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