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Zero-shot learning for visual recognition has received much interest in the most recent years. However, the semantic gap across visual features and their underlying semantics is still the biggest obstacle in zero-shot learning. To fight off this hurdle, we propose an effective Low-rank Embedded Semantic Dictionary learning (LESD) through ensemble strategy. Specifically, we formulate a novel framework...
We propose to jointly learn a Discriminative Bayesian dictionary along a linear classifier using coupled Beta-Bernoulli Processes. Our representation model uses separate base measures for the dictionary and the classifier, but associates them to the class-specific training data using the same Bernoulli distributions. The Bernoulli distributions control the frequency with which the factors (e.g. dictionary...
The main challenge for anomaly detection in Self-Organizing Industrial Systems (SOIS) is the high degree of freedom of the system, which causes a state-space explosion. Since the system is free to choose at runtime any solution out of the vast amount of possible ones, to ensure that the production process is optimal at all times, classic anomaly detection techniques can not be used one-to-one in SOISs...
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained...
Broad Learning System [1] proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance...
Face recognition has been an important task in pattern recognition and computer vision. Recently, sparse representation has become a popular data representation method in face recognition field. Convolutional sparse coding, which replaces the linear combination of a set of dictionary atoms with the sum of s series of mapping term convoluted with the dictionary filters, was proposed to improve the...
Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative...
Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate use of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel...
An improved super-resolution image reconstruction algorithm based on dictionary-learning is studied for the time-consuming algorithms in the existing dictionary training process. In this paper, the reconstruction of image super resolution is realized from the compressed sensing theory. The image patches are conveyed by sparse linear representations with an over-complete dictionary. In the process...
Discriminative dictionary learning aims to learn a dictionary from training samples in order to improve the discriminative ability of their coding vectors. Gabor wavelets have recently been successfully applied for hyperspectral image (HSI) classification due to their ability to extract joint spatial and spectrum information. Due to the high discriminative power of Gabor features, an efficient method,...
In this paper, a new subpixel mapping approach for hyperspectral image is proposed, using a spatial-spectral endmember dictionary with collaborative representation (CR). Different from the classic approaches, the proposed approach employ several spatially closest training samples as the endmembers used for the representation of each mixed pixel, instead of the entire training set. Furthermore, the...
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based...
Learning-based face super-resolution approaches rely on representative dictionary as self-similarity prior from training samples to estimate the relationship between the low-resolution (LR) and high-resolution (HR) image patches. The most popular approaches, learn mapping function directly from LR patches to HR ones but neglects the multi-layered nature of image degradation process (resolution down-sampling)...
Image annotation is always an easy task for humans but a tough task for machines. Inspired by human's thinking mode, there is an assumption that the computer has double systems. Each of the systems can handle the task individually and in parallel. In this paper, we introduce a new hierarchical model for image annotation, based on constructing a novel, hierarchical tree, which consists of exploring...
In this paper, we address fine-grained classification which is quite challenging due to high intra-class variations and subtle inter-class variations. Most modern approaches to fine-grained recognition are established based on convolutional neural networks (CNN). Despite the effectiveness, these approaches still suffer from two major problems. First, they highly rely on large sets of training data,...
Several models based on deep neural networks have applied to single image super-resolution and obtained great improvements in terms of both reconstruction accuracy and computational performance. All these methods focus either on performing the super-resolution (SR) reconstruction operation in the high resolution (HR) space after upscaling with a single filter, usually bicubic interpolation, or optimizing...
This paper describes a novel algorithm to improve the performance of sparsity based single-channel speech separation(SCSS) problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The conventional approach assumes the mixing conditions and source signals are stationary. For practical applications of audio source separation, however, we face the challenges...
In this paper, we propose a new single sample face recognition approach under the widely used sparse representation-based classification (SRC) framework. Previous work has shown that SRC only works well when there are sufficient number of training samples per person and not suitable for SSFR. To address this, we propose a domain transfer sparse representation-based classification (DT-SRC) method by...
We present VidedWhisfer, a novel approach for unsupervised video representation learning, in which video sequence is treated as a self-supervision entity based on the observation that the sequence encodes video temporal dynamics (e.g., object movement and event evolution). Specifically, for each video sequence, we use a pre-learned visual dictionary to generate a sequence of high-level semantics,...
Learning-based image super-resolution methods often use large datasets to learn texture features. When these methods are applied to depth images, emphasis should be given on learning the geometrical structures at object boundaries, since depth images do not have much texture information. In this paper, we develop a scheme to learn multiple residual dictionaries from only one external image. After...
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