Structured sparse priors for image classification.

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.

Graph-based sensor fusion for classification of transient acoustic signals.

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.

Simultaneous sparsity model for histopathological image representation and classification.

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.

Development and evaluation of an automated histology classification system for veterinary pathology.

A 2-stage algorithmic framework was developed to automatically classify digitized photomicrographs of tissues obtained from bovine liver, lung, spleen, and kidney into different histologic categories. The categories included normal tissue, acute necrosis, and inflammation (acute suppurative; chronic). In the current study, a total of 60 images per category (normal; acute necrosis; acute suppurative inflammation) were obtained from liver samples, 60 images per category (normal; acute suppurative inflammation) were obtained from spleen and lung samples, and 60 images per category (normal; chronic inflammation) were obtained from kidney samples. An automated support vector machine (SVM) classifier was trained to assign each test image to a specific category. Using 10 training images/category/organ, 40 test images/category/organ were examined. Employing confusion matrices to represent category-specific classification accuracy, the classifier-attained accuracies were found to be in the 74-90% range. The same set of test images was evaluated using a SVM classifier trained on 20 images/category/organ. The average classification accuracies were noted to be in the 84-95% range. The accuracy in correctly identifying normal tissue and specific tissue lesions was markedly improved by a small increase in the number of training images. The preliminary results from the study indicate the importance and potential use of automated image classification systems in the histologic identification of normal tissues and specific tissue lesions.