Dictionary use can improve reading comprehension and incidental vocabulary learning. Nevertheless, great extraneous cognitive load imposed by the search process may reduce or even prevent the improvement. With the help of technology, dictionary users can now instantly access the meaning list of a searched word using a mouse click. However, they must spend great cognitive effort identifying the ...

A complete and discriminative dictionary can achieve superior performance. However, it also consumes extra processing time and memory, especially for large datasets. Most existing compact dictionary learning methods need to set the dictionary size manually, therefore an appropriate dictionary size is usually obtained in an exhaustive search manner. How to automatically learn a compact dictionar...

Representing objects using elements from a visual dictionary is widely used in object detection and categorization. Prior work on dictionary learning has shown improvements in the accuracy of object detection and categorization by learning discriminative dictionaries. However none of these dictionaries are learnt for joint object categorization and segmentation. Moreover, dictionary learning is...

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 ...