Recently, super-resolution reconstruction (SRR) method of
low-dimensional face subspaces has been proposed for
face recognition. However, the reconstructed features obtained from the face-specific super-resolution subspace
contain no class information. This paper proposes a novel method for super-resolution reconstruction of class specific features that aims on improving the discriminant power of the recognition systems. Our experimental results on Yale and ORL face databases are very encouraging. Furthermore, the performance of our proposed
approach on the MSTAR database is also tested for preliminary evaluation.
KEYWORDS: Image compression, Wavelets, Visualization, Mobile communications, Image analysis, Video compression, 3D image processing, Wavelet transforms, 3D video compression, Visual compression
In this paper, we present an efficient compression technique that is suitable for image/video communications over wireless (mobile) channel. Our technique uses basic directional differences operators to estimate corresponding detail subband images/videos from their approximation subband images/videos. We empirically found that the detail subband images/videos can be well approximated by the estimate subband images/videos. In this work, image and video are first decomposed using integer wavelet packet transformation. Having established that detail subband images/video can be estimated from the approximation subbands, the information needed to send over the wireless channel is only the most important subband images/video, where we selected them via best basis selection algorithm. Next, after best basis selection, the selection subband components are encoded using either SPIHT (JPEG) for image or 3-D SPIHT for video and then the encoded data are sent over the wireless channel. The advantages of our algorithms are two folds. First, most of the computation used in our technique is performed in integer for the purpose of coding speed improvement. Second, the computation of our algorithm (either SPIHT (JPEG) or 3-D SPIHT) is reduced from its original computation by an order of magnitude. The reason is that in our algorithm either SPIHT (JPEG) or 3-D SPIHT is performed only on the set of important components (two or a few subband image/videos) instead of the whole image/video. Finally, we show that our proposed algorithm using SPIHT (3-D SPIHT) are better that pure JPEG (MPEG-2) both in terms of human visual image and computation complexity.
KEYWORDS: Systems modeling, Automatic target recognition, Monte Carlo methods, Synthetic aperture radar, Wavelets, Image compression, Signal processing, Detection and tracking algorithms, Image classification
In this paper, we propose a new multiple classifier system (MCS) based on two concatenated stages of multiple description coding models (MDC) and multiple description sampling (MDS). This paper draws on concepts coming from a variety of disciplines that includes classical concatenated coding of error correcting codes, multiple description coding of wavelet based image compression, Adaboost and importance sampling of multiple classifier systems, and antithetic-common varaites of Monte Carlo Methods. In our previous work, we proposed and extended several methods in MDC to MCS with inspirations from two frameworks. First, we found that one of our methods is equivalent to one of the variance reduction techniques, called antithetic-common variates, in the Monte Carlo Methods (MCM). Having established that Adaboost can be interpreted as important sampling in MCM, and it can directly be interpreted as MDC, we define the term "multiple description sampling (MDS)" for Adaboost. Second, another equivalent relation between one of our methods and transmitting data over heterogeneous network, especially wireless networks, are established. One of the benefits of our approach is that it allows us to formulate a generalized class of signal processing based weak classification algorithms. This will be very applicable for MDC-MDS in high dimensional classification problems, such as image/target recognition. Performance results for automatic target recognition are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our proposed method outperform state-of-the-art multiple classifier systems, such as Adaboost and SVM-ECOC etc.
KEYWORDS: Automatic target recognition, Monte Carlo methods, Image classification, Synthetic aperture radar, Wavelets, Signal processing, Detection and tracking algorithms, Systems modeling, Image compression
In this paper, we propose a new multiple classifier system (MCS) based on two concatenated stages of multiple description coding models (MDC) and Support Vector Machine (SVM). This paper draws on concepts coming from a variety of disciplines that includes classical concatenated coding of error correcting codes, multiple description coding of wavelet based image compression, error correcting output codes (ECOC) of multiple classifier systems, and antithetic-common varaites of Monte Carlo Methods. In our previous work, we proposed and extended several methods in MDC to MCS with inspirations from two frameworks. First, we found that one of our methods is equivalent to one of the variance reduction techniques, called antithetic-common variates, in the Monte Carlo Methods (MCM). Second, another equivalent relation between one of our methods and transmitting data over heterogeneous network, especially wireless networks, are established. In this paper, we also include several support ideas. For example, preliminary surveys on the biological plausible of the MDC concepts are also included. One of the benefits of our approach is that it allows us to formulate a generalized class of signal processing based weak classification algorithms. This will be very applicable for MDC-SVM in high dimensional classification problems, such as image/target recognition. Performance results for automatic target recognition are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our proposed method outperform state-of-the-art multiple classifier systems, such as SVM-ECOC etc.
In this paper, the possibility of using an orthogonal basis to train a collection of artificial neural networks in a face recognition task is discussed. This orthonormal basis is selected from a dictionary of orthonormal bases consisting of wavelet packets. Here, a basis is obtained by maximizing a certain discriminant measure among classes of training images. Once such a basis is selected, its basis vectors are ordered according to their power of discrimination and the first N most local discriminant basis vectors are retained for image decomposition purpose. By projecting all training images onto an individual basis vector of these N most discriminant basis vectors, N versions of the training set at different spatial/scale resolutions are then created. Next, N multilayer feed- forward neural networks are trained independently by N different resolution-specific training sets. After networks have been trained, they are combined to form an ensemble of networks. Our proposed method takes advantage of the fact that the dimensionality of the pattern recognition problem at hand is reduced, but the important information is still contained, and at the same time, some correlations between neighboring inputs are included. Furthermore, the performance of our proposed network is improved over a single neural network as a result of the ensemble and the nonlinear property of neural networks. Finally, this method is applied to a face recognition task using the Yale Face Database. From the experimental results , the performance of our method is better than a conventional back-propagation network and a wavelet packet parallel consensual neural network in terms of both computation and generalization ability.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.