Abstract. Sparse coding plays a key role in high dimensional data anal-ysis. One critical challenge of sparse coding is to design a dictionary that is both adaptive to the training data and generalizable to unseen data of same type. In this paper, we propose a novel dictionary learning method

(CRF) model for semantic segmentation. Recent work shows that dis-criminative sparse dictionary learning (DSDL) can improve upon the unsupervised K-means dictionary learning method used in the BoF model due to the ability of DSDL to capture dis-criminative features from different classes. However

constrained sparse dictionary learning

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The structure is based on a sparsity model of the dictionary atoms over a base dictionary. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ΦA where Φ is a fixed base dictionary and A is sparse

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Abstract not found

Abstract — This paper presents a new eye localization method via Multiscale Sparse Dictionaries (MSD). We built a pyramid of dictionaries that models context information at multiple scales. Eye locations are estimated at each scale by fitting the image through sparse coefficients of the dictionary

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

encodes each input image over a dictionary specifically trained for it. The scheme is based on the sparse dictionary structure, whose compact representation allows relatively low-cost transmission of the dictionary along with the compressed data. In this way, the process achieves both adaptivity