We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly l...

We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of a scene has a sparse linear representation over a learned dictionary; (2) the foreground is “sparse” in the sense that majority pixels of the frame belong to the background. These two ass...

Expressing data vectors as sparse linear combinations of basis elements (dictionary) is widely used in machine learning, signal processing, and statistics. It has been found that dictionaries learned from data are more effective than off-the-shelf ones. Dictionary learning has become an important tool for computer vision. Traditional dictionary learning methods use quadratic loss function which...

Dictionary Learning and sparse coding methods have been widely used in computer vision with applications to face and object recognition. A common challenge when performing expression recognition is that face similarities may confound the expression recognition process. An approach to deal with this problem is to learn expression specific dictionaries, so that each atom corresponds to one expres...

Extracting sparse representations with Dictionary Learning (DL) methods has led to interesting image and speech recognition results. DL has recently been extended to supervised learning (SDL) by using the dictionary for feature extraction and classification. One challenge with SDL is imposing diversity for extracting more discriminative features. To this end, we propose Incrementally Built Dict...

Recent research has shown that students involved in computer-based second language learning prefer to use a digital dictionary in which a word can be looked up by clicking on it with a mouse (i.e., click-on dictionary) to a digital dictionary in which a word can be looked up by typing it on a keyboard (i.e., key-in dictionary). This study investigated whether digital dictionary format also diff...

As technology develops, the prevalence of conventional book dictionaries has slowly declined due to advancements in computer-mediated aids, such as online type-in dictionaries and program-installed pop-up aids. The goal of this study was to examine how technology ‘‘may” have changed the long-standing pedagogical practice of book dictionary usage by identifying the learning processes associated ...

We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and cl...

In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. Unlike traditional dictionary learning, our method integrates both global and patch-wise (local) sparsity information and incorporates some priori information into the reconstruction process. Moreover, we use a Depende...

We present a new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data. The proposed DDLCN has most of the standard layers (e.g., input/output, pooling, fully connected), but fundamental convolutional are replaced by our compound layers. learns an overcomplete input training At layer, locality constraint is added to guarantee that activated bases clos...