Sparse representation has been successfully applied to pattern recognition problems in recent years. The most common way for producing sparse coding is to use the l1-norm regularization. However, the l1-norm regularization only favors sparsity and does not consider locality. It may select quite different bases for similar samples to favor sparsity, which is disadvantageous to classification. Besides, solving the l1-minimization problem is time consuming, which limits its applications in large-scale problems. We propose an improved algorithm for sparse coding and dictionary learning. This algorithm takes both sparsity and locality into consideration. It selects part of the dictionary columns that are close to the input sample for coding and imposes locality constraint on these selected dictionary columns to obtain discriminative coding for classification. Because an analytic solution of the coding is derived by only using part of the dictionary columns, the proposed algorithm is much faster than the l1-based algorithms for classification. Besides, we also derive an analytic solution for updating the dictionary in the training process. Experiments conducted on five face databases show that the proposed algorithm has better performance than the competing algorithms in terms of accuracy and efficiency.
In this paper we present an approach for face recognition based on Hidden Markov Model (HMM) in
compressed domain. Each individual is regarded as an HMM which consists of several face images. A
set of DCT coefficients as observation vectors obtained from original images by a window are clustered
by K-means method using to be the feature of face images. These classified features are applied to train
HMMs, so as to get the parameters of systems. Based on the proposed method, both Yale face database
and ORL face database are tested. Compared to the other methods relevant to HMM methods reported
so far on the two face databases, experimental results by proposed method have shown a better
recognition rate and lower computational complexity cost.
The explosive growth of images and videos on the World Wide Web (WWW) is making the Web into a huge resource of visual information. Among various types of multimedia information, still images or dynamic images (video clips) in compressed format are the most widely accepted on the WWW. Therefore, it becomes an essential issue to achieve the maximum efficiency in transmitting and decoding those compressed images on the Internet. Progressive coding provides a mode that allows a coarse version of an image being transmitted at a lower bit rate and then gradually refined by subsequent transmissions. Compared with conventional coding, it is more suitable for interactive applications such as those involving JPEG images on the Internet. In this paper, we first give an approximation of cosine function used in IDCT for the various orders. Based on the approximation and a series analysis, we then develop a progressive decoding scheme which comprehends the successive approximation and the spectral selection. The analysis and experiments establish the fact that our proposed method saves computational cost significantly in comparison with the existing spectral selection based progressive decoding proposed by JPEG. Extensive experiments are carried out to evaluate the proposed algorithm, which reveals that, the reconstructed images, even at the lowest bit rate and with lower order approximation, can still achieve encouraging PSNR values.