Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches.

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of class-specific spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.

Dictionary learning techniques have gained tremendous success in many classification problems. Inspired by the dirty model for multi-task regression problems, we proposed a novel method called group-structured dirty dictionary learning (GDDL) that incorporates the group structure (for each task) with the dirty model (across tasks) in the dictionary training process. Its benefits are two-fold: 1) the group structure enforces implicitly the label consistency needed between dictionary atoms and training data for classification; and 2) for each class, the dirty model separates the sparse coefficients into ones with shared support and unique support, with the first set being more discriminative. We use proximal operators and block coordinate decent to solve the optimization problem. GDDL has been shown to give state-of-art result on both synthetic simulation and two face recognition datasets.

The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e. rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as recently proposed joint sparsity models for multi-sensor acoustic signal classification.

Sparse modeling has demonstrated its superior performances in many applications. Compared to optimization based approaches, Bayesian sparse modeling generally provides a more sparse result with a knowledge of confidence. Using the Spike and Slab priors, we propose the hierarchical sparse models for the scenario of single task and multitask - Hi-BCS and CHi-BCS. We draw the connections of these two methods to their optimization based counterparts and use expectation propagation for inference. The experiment results using synthetic and real data demonstrate that the performance of Hi-BCS and Chi-BCS are comparable or better than their optimization based counterparts.

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, the Laplacian being the most common such choice (leading to l1-norm minimization). The recent seminal contribution by Wright et al. exploits the discriminative capability of sparse representations for image classification, specifically face recognition. Their approach employs the analytical framework of CS with class-specific dictionaries. Our contribution is a logical extension of these ideas into structured sparsity for classification. We use class-specific dictionaries in conjunction with discriminative class-specific priors, specifically the spike-and-slab prior widely applied in Bayesian regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes.

A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.

The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures class-specific discriminative information crucial for detection tasks. Spatio-spectral information has also been introduced into this framework via a joint sparsity model that simultaneously solves for the sparse features for a group of spatially local pixels, since such pixels are highly likely to have similar spectral characteristics. In this paper, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between these distinct sparse representations corresponding to different pixels in a spatial neighborhood. Simulation results show that the proposed algorithm outperforms classical hyperspectral target detection algorithms as well as support vector machines.

Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many applications, these densities can only be approximated due to limited training data or dynamic changes affecting the observed signal. A classical approach to handle such scenarios of imprecise knowledge is via minimax robust hypothesis testing (RHT), where a test is designed to minimize the worst case performance for all models in the vicinity of the approximated imprecise density. Despite the promise of RHT for robust classification problems, its applications have remained rather limited because RHT in its native form does not scale gracefully with the dimension of the observed random vector. In this paper, we use approximations via probabilistic graphical models, in particular block-tree graphs, to enable computationally tractable algorithms for realizing RHT on high-dimensional data. We quantify the reductions in computational complexity. Experimental results on simulated data and a target recognition problem show minimal loss over a true RHT.

Of recent interest in automatic target recognition (ATR) is the problem of combining the merits of multiple classifiers. This is commonly done by “fusing” the soft-outputs of several classifiers into making a single decision. We observe that the improvement in recognition rates afforded by these approaches is due to the complementary yet correlated information captured by different features/signal representations that these individual classifiers employ. We present the use of probabilistic graphical models in modeling and capturing feature dependencies that are crucial for target classification. In particular, we develop a two-stage target recognition framework that combines the merits of distinct and sparse signal representations with discriminatively learnt graphical models. The first stage designs multiple projections yielding M >; 1 sparse representations, while the second stage models each individual representation using graphs and combines these initially disjoint and simple graphical models into a thicker probabilistic graphical model. Experimental results show that our approach outperforms state-of-the art target classification techniques in terms of recognition rates. The use of graphical models is particularly meritorious when feature dimensionality is high and training is limited - a commonly observed constraint in synthetic aperture radar (SAR) imagery based target recognition.