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The supervised dictionary learning methods have made considerable achievements in the field of pattern recognition. In order to make the learned coefficients have intraclass similarity and interclass incoherence, many dictionary learning methods have been proposed using different structured constraints. However, these constraints often have the disadvantages of complexity, time-consuming, and poor interpretability. More importantly, the existing dictionary learning often ignores the inherent structure of the label matrix. In this paper, we propose a dictionary pair learning based on the structured classifier and perform classifier learning and structured coefficients learning simultaneously. Our main idea is to use the extended label matrix and the invertible constraints on the classification transformation matrix to utilize the structure of the label matrix to impose structured constraints on coefficients. Moreover, the l 21 -norm is added to force the analysis dictionary to focus on more important features. Experimental results on multiple databases prove that our proposed method has better classification performance than several state-of-the-art dictionary learning methods. [ABSTRACT FROM AUTHOR]

Dimensionality reduction plays a major role in face recognition. Discriminant analysis (DA) and principal component analysis (PCA) are two of the most important approaches in this field. In particular, subclass discriminant analysis (SDA) is a well-known scheme for feature extraction and dimensionality reduction. It is widely used in many high-dimensional data-driven applications, namely face recognition and image retrieval. It is also found to be applicable under various scenarios. However, it has high cost in time and space given the need for an eigendecomposition involving the scatter matrices, known as the singularity problem. This limitation is caused by the high-dimensional space of data, particularly when dimensions exceed the number of observations. Recent advances widely reported that 2D methods with matrix-based representation perform better than the traditional 1D vector-based ones. In this paper, we propose a novel 2D-SDA algorithm to avoid the "curse of dimensionality" and address the singularity issue. The performance of the proposed algorithm is evaluated for face recognition in terms of recognition performance and computational cost. Experiments are conducted on four benchmark face databases and compared to several competitive 1D and 2D methods based on PCA and DA. Results show that 2DSVD achieves the best recognition performance at low dimensions. In particular, 2D-SDA works significantly better on large-sized data sets where intra-class variation is the most important. [ABSTRACT FROM AUTHOR]

Moment invariants that are not affected by geometric transform have been utilized as pattern features in a number of applications. But in most cases, images are processed subject to blur degradations. The traditional blur invariant sets were constructed using geometric moments, central moments or complex moments. However, these non-orthogonal moments are generally considered as a disadvantage over orthogonal moments, such as Zernike, pseudo-Zernike, and Legendre moments, in decreasing information redundancy and sensitivity to noises. To solve this problem, this paper addresses a method for recognizing objects in an image in a way that is invariant to images’ blur and rotation transformations to improve the robustness to noises. The proposed method is based on Zernike descriptors which are orthogonal over a unit circle, and is invariant to a central symmetric blur, such as linear motion or out-of-focus blur. We present a mathematical framework of obtaining the Zernike moments of blurred images, and a framework of deriving the combined blur and rotation invariants. The classification experimental results are presented to confirm the proposed method outperforms other similar ones in the presence of various blur-degraded and rotation-transformed images. [ABSTRACT FROM AUTHOR]