The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in...

Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications. Here we propose to use the feature learning pipeline for visual tracking. Tracking is implemented using tracking-bydetection and the resulted framework is very simple yet effe...

In recent years, the creation of block-structured dictionary has attracted a lot of interest. Learning such dictionaries involve two step process: block formation and dictionary update. Both these steps are important in producing an effective dictionary. The existing works mostly assume that the block structure is known a priori while learning the dictionary. For finding the unknown block struc...

We introduce an Entropy-Constrained OvercompleteBased coding scheme for natural images. The traditional overcomplete-based framework for compression is improved in its main components. The main contribution of the work is a new dictionary learning algorithm for overcomplete-based compression, referred as Entropy-Constrained Dictionary Learning. We show that the presented scheme outperforms a ba...

A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbsslice sampler, and encouraging results are reported on widely used datasets.

We explore the use of dictionary-based approaches for cross-lingual information retrieval tasks and propose a novel Coupled Dictionary Learning (CDL) algorithm to learn two separate representations simultaneously for documents in a parallel corpus alongside learning mappings from one representation to the other. We evaluate the performance of the proposed algorithm for the task of comparable do...

In many applications of compressive sensing, the dictionary providing the sparse description is partially or entirely unknown. It has been shown that dictionary learning algorithms are able to estimate the basis vectors from a set of training samples. In some applications the dictionary is multidimensional, e.g., when estimating jointly azimuth and elevation in a 2-D direction of arrival (DOA) ...

In this paper, we propose a novel dictionary learning method in the semi-supervised setting by dynamically coupling graph and group structures. To this end, samples are represented by sparse codes inheriting their graph structure while the labeled samples within the same class are represented with group sparsity, sharing the same atoms of the dictionary. Instead of statically combining graph an...

Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the `0 or `1-norm sparsity constraint on the representation coefficients adopted ...

There has been significant recent interest in dictionary learning and sparse coding, with applications in denoising, interpolation, feature extraction, and classification [1]–[3]. Increasingly it has been recognized that these models may be improved by imposing additional prior information, beyond sparseness. For example, a locality constraint has been used successfully in the context of featur...