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Convolutional sparse coding is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations. Although several works have addressed the dictionary learning problem under this model, these relied on an ADMM formulation in the Fourier domain, losing the sense of locality and the relation to the traditional...
In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring outside of the network (as most methods do), we propose a compact and elegant end-to-end deblurring network. Inspired by the data-driven sparse-coding approaches that are capable of capturing linear dependencies in data, we generalize...
Traditional image classification methods require the independence and the same distribution of training and testing data. However, this requirement cannot always be satisfied in some real-world applications, especially for crossdomain image classification tasks. In this paper, we propose to deal with this problem by combining transfer learning with sparse coding and dictionary learning. In dictionary...
Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands of images per class for training is infeasible in such a scenario, prompting the use of webly supervised data. This paper explores the training of image-recognition systems on large numbers of images and associated user...
Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter...
The topic of representation, classification, and clustering of text documents and information extraction is currently a very researched area. The area of data mining and text mining has its specific problems in the Slovak language. This paper deals with the methods of pre-processing of medical data, namely Slovak health records written in natural language, and their subsequent analysis, especially...
Sparse representation is a signal processing method which is mostly used in signal compression, noise reduction, and signal and image restoration fields. In this study, sparse representation was used in a different way from the traditional methods. In the proposed method, a hybrid structure was created by combining dictionary learning and ensemble classifier AdaBoost algorithms. The main idea of this...
There are many challenges in single-channel multi-person mixed speech separation, such as modeling the temporal continuity of the speech signals and improving the frame separation performance simultaneously. In this paper, a separation method based on Deep Clustering with local optimization by the improved Non-Negative Matrix Factorization (NMF) combined with Factorial Conditional Random Fields (FCRF)...
Scene recognition is an important and challenging problem in the field of computer vision owing to the variations in the same class and the similarities between different classes. This paper presents a novel approach that learns a reasonable dictionary from convolutional features to effectively describe the distinctive and shared properties in scene images. Substantial convolution operations in Deep...
This paper introduces the use of representations based on nonnegative matrix factorization (NMF) to train deep neural networks with applications to environmental sound classification. Deep learning systems for sound classification usually rely on the network to learn meaningful representations from spectrograms or hand-crafted features. Instead, we introduce a NMF-based feature learning stage before...
In traditional text sentiment analysis methods, text feature vector has the problem of high dimensionality and high sparseness. In view of this situation, we can cluster the similar words together and use the generated clusters to fit into a new dimension so that the text feature vector dimension will be decreased. By using Word2Vec tool and K-means clustering algorithm, this task can be completed...
The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients...
Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization...
This paper proposes a novel method based on the archetypal analysis (AA) for bird activity detection (BAD) task. The proposed method extracts a convex representation (frame-wise) by projecting a given audio signal on to a learned dictionary. The AA based dictionary is trained only on bird class signals, which makes the method robust to background noise. Further, it is shown that due to the inherent...
In this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms...
In recent years, intelligent mathematics problem solving has aroused the interest of researchers. In the intelligent mathematics problem solving system related to high school, the classification of statistical graph is a key step. Consequently, the classification of statistical graphs has become an urgent problem to be solved. In this paper, a new method is proposed for statistical graphs classification...
Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical...
Compressed sensing is a signal acquisition scheme that measures signals at sub-Nyquist rate amenable to sparse recovery, with high probability, from a reduced set of measurements. One of the main requirements of compressive sensing is the sparsity of the class of signals of interest in some basis. A method to construct a sparsifying basis for a class of signals using information theoretic measures...
We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights...
Neighbors embedding is a promising method for single image super-resolution (SR). However, the fixed number of neighbors for different kind of input low resolution (LR) patches may be improper. In addition, the assumption that low resolution space and high resolution (HR) space has similar local geometry leads to improper HR patches are used for reconstruction. In this paper, we propose a novel single...
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