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Embedded computer vision applications have been incorporated in industrial automation, improving quality and safety of processes. Such systems involve pattern classifiers for specific functions that, many times, demand high memory footprint and processing time. This work suggests a strategy to choose GLCM (Gray Level Co-occurrence Matrix) features for an SVM classifier that can reduce computer resources...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes...
We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply...
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...
With the development of smart phones, more and more mobile phone malwares have came out in the market especially on the popular platforms such as Android, which can potentially cause harm to users' information. But how to effectively detect the new malwares and malicious software variants has been a difficult problem. In view of the traditional feature extraction method based on binary program, this...
The color constancy problem is addressed by structured-output regression on the values of the fully-connected layers of a convolutional neural network. The AlexNet and the VGG are considered and VGG slightly outperformed AlexNet. Best results were obtained with the first fully-connected “fc6” layer and with multi-output support vector regression. Experiments on the SFU Color Checker and Indoor Dataset...
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...
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...
This paper presents the construction of Binary Support Vector Machines and its significance for efficient Speech Emotion Recognition (SER). German Emotional Speech Corpus EmoDB has been used in this study. Seven Binary Support Vector Machines (SVMs) corresponding to each of the seven emotions in the EmoDB, namely Anger-Not Anger, Boredom-Not Boredom, Disgust-Not Disgust, Fear-Not Fear, Happy-Not Happy,...
As the present fusing strategies cannot utilize the correlation of different detection results for image steganography effectively, a steganalysis method is proposed based on fusing SVM classifiers. Firstly, different feature subsets are used for the training of SVM classifiers. Secondly, the detection results of multi-classifiers are utilized to train a fusing classifier, the fusing classifier can...
In the view of the characteristic of the imbalanced microanuerysm candidate datasets: a large number of negative samples, the different distributions of different classes and the irrelevant features exacted from each candidate for learning task, this paper proposes a feature selection algorithm that we selected the top features out of all features that were ranked in the increasing order of feature...
In this paper, we analyze the relationships between social popularity (i.e., the numbers of views, comments, and favorites) and text tags in image/video sharing services. We also show the tags which affect social popularity in each service and discuss the characteristics of popular contents in each service by analyzing these results.
Fingerprint recognition systems are vulnerable to spoof attacks, which consist in presenting forged fingerprints to the sensor. Typical anti-spoofing mechanism is fingerprint liveness detection. Existing liveness detection methods are still not robust to spoofing materials, datasets and sensor variations. In particular, the performance of a liveness detection algorithm remarkably drops upon encountering...
Recently there has been great interest in the application of word representation techniques to various natural language processing (NLP) scenarios. Word representation features from techniques such as Brown clustering or spectral clustering are generally computed from large corpora of unlabeled data in a completely unsupervised manner. These features can then be directly included as supplementary...
Automated quality control of produce such as fruits and vegetables is of great importance to industry. In particular, the ability to evaluate the state of decay for various produce items would allow for efficient sorting of produce such that the freshest items could be more quickly shipped to consumers. Unfortunately, training an accurate classifier for determining how decayed produce is can require...
Walking is the most easily achievable way to reduce obesity and to keep fit. Step counting devices are helpful for users to develop regular walking habits, but they may mistake some abnormal situations (SHAKING, SWING, JITTER, PENDULUM, etc.) for walking and thus result in step counting errors. Such errors will inflate the number of steps, and lead to biased inter-user comparisons or competitions...
Functional magnetic resonance imaging (fMRI) aims to localize task-related brain activation or resting-state functional connectivity. Most existing fMRI data analysis techniques rely on fixed thresholds to identify active voxels under a task condition or functionally connected voxels in the resting state. Due to fMRI non-stationarity, a fixed threshold cannot adapt to intra- and inter-subject variation...
Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection...
The availability and exponential growth in online media provides opportunities for understanding and responding to real world challenges. In this paper we investigate the photo quality assessment problem using a large volume of online images retrieved by Google Image Search. To effectively use the big data, we present new approaches that compute discriminative features from a group of relevant images...
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