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Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within- class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition...
In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
Successful ECG monitoring algorithms often rely on learned models to describe the heartbeats morphology. Unfortunately, when the heart rate increases the heartbeats get transformed, and a model that can properly describe the heartbeats of a specific user in resting conditions might not be appropriate for monitoring the same user during everyday activities. We model heartbeats by dictionaries yielding...
Interactions between drugs (also known as drug-drug interactions or DDIs), which may cause adverse affects, are of much concern; predicting, anticipating and avoiding them is key for improving patient safety and treatment outcome. Knowledge of DDIs is important for physicians to avoid adverse effects when prescribing two drugs simultaneously. DDIs are often published in the biomedical literature;...
In conventional dictionary learning, the class label of the atoms is not retained. As a result of that, the location of non-zeros elements in the sparse vector (s-vector) does not infer about the true class of the test vector unlike the sparse representation classification (SRC) over the exemplar dictionary. Thus, in our earlier works employing the learned dictionary for language recognition (LR),...
As an SNS, Twitter is popular because users can post their emotions as a short message easily. Emotional tweets may influence user relationships. In our previous study, we found that positive users construct mutual relationships in Twitter. Keyword matching with emotional word dictionaries was used to detect positive users. The problem of keyword matching is the limitation of word number. To solve...
Human action recognition is one of the most active research areas of computer vision. With the rapid development of deep learning, using neural networks to realize action recognition becomes a popular thesis. This paper proposes a self-learned action recognition method based on neural networks. The proposed method trains dictionaries with sparse autoencoder (SAE) and extracts the key frames with sparse...
To realize Electrocardiography (ECG) signals monitoring systems, compressive sensing (CS) is a new technique to reduce power of biosensors and data transmission. Instead of spending high complexity on reconstructing back to data domain to do signal analysis, compressed analysis (CA) exploits the data structure preserved by CS to directly analyze in the compressed domain. However, compressively-sensed...
In this article, we present a target speaker dependent speech enhancement system, to enhance a specific target talker in presence of real life background noises. The proposed system uses a multi-channel processing stage to produce a noise reference signal. This noise reference signal is further used, to not only compute the residual noise statistics from the multichannel stage output, but also to...
Sentiment Analysis tools, developed for analyzing social media text or product reviews, work poorly on a Software Engineering (SE) dataset. Since prior studies have found developers expressing sentiments during various SE activities, there is a need for a customized sentiment analysis tool for the SE domain. On this goal, we manually labeled 2000 review comments to build a training dataset and used...
This work proposes a novel password guessing approach based on the identification, extraction and recombination of meaningful syntactic patterns present in human-chosen passwords. The proposed method exploits the existence of these patterns across user-selected passwords in order to effectively reduce the search space to be explored during the password guessing process. The password guessing scheme...
Action recognition is still a challenging problem. In order to catch effective compact representation of the action sequences, the discriminative dictionaries could be learned by sparse coding. But sparse coding is needed in both the training and testing phases of the classifier framework. And it is also time consuming for the adoption of 1-norm sparsity constraint on the representation coefficients...
The field of opinion mining is expanding rapidly with the widespread use of internet for e-commerce and social interaction. One of the interesting use of opinion mining is in the field of online producer-consumer industry. The primary goal of the work presented in this paper is to perform a semi-automated sentiment classification on online product reviews for product evaluation using machine learning...
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...
In this paper, we propose a novel face recognition method that embeds the locality-constrained sparse representation in the dictionary learning framework. The shared-specific dictionary learning is employed to explicitly learn class-specific dictionary for each class that captures the most discriminative features of this class, and simultaneously learn a shared dictionary, whose atoms are shared by...
While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting...
Many face recognition tasks encounter the problem of having only one sample for each subject, which is known as the single sample per person (SSPP) problem. To tackle the problem, we propose a strategy of sparse representation with dense matching method. First, an external training set is used to form an intra-class variation dictionary. Then, noting that captured facial features will vary with facial...
A novel framework in image sparse representation based on ensemble learning is proposed in this paper. Due to the random extraction of training patches and the variation of single optimal processing result, the proposed scheme develops classical dictionary learning algorithms in compressed sensing with ensemble learning theory to improve the performance of sparse representation. The analysis of computational...
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