Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

The dictionary

The Portia Geach Memorial Award is a celebration of female Australian artists. It’s Australia’s most prestigious portrait prize for female artists and has greatly contributed to the development of female artists in this country. It was established by Florence Kate Geach in 1961 in memory of her sister, Portia Geach and is awarded each year to the best portrait painted from life of some man or woman distinguished in Art, Letters or the Sciences. Our judges this year are Jane Watters, Director S.H. Ervin Gallery, Dr Lindy Lee, Senior Lecturer, Sydney College of the Arts and Ben Quilty, Artist. A media release with the full list of finalists will be distributed tomorrow morning with further details available at www.thetrustcompany.com.au/portiageach. The artwork The Dictionary will be hung in the S.H. Ervin Gallery from 3 October until 16 November, with the exhibition open from 4 October, 1st Prize money $30,000

Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform

How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.

Junior English - Arabic Maths Dictionary

Junior English - Arabic Maths Dictionary