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This paper presents a nonlinear subspace clustering (NSC) method for image clustering. Unlike most existing subspace clustering methods which only exploit the linear relationship of samples to learn the affine matrix, our NSC reveals the multi-cluster nonlinear structure of samples via a nonlinear neural network. While kernel-based clustering methods can also address the nonlinear issue of samples, this type of methods suffers from the scalability issue.

Classification of plants based on a multi-organ approach is very challenging. Although additional data provides more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Existing approaches focus mainly on generic features for species classification, disregarding the features representing the organs. In fact, plants are complex entities sustained by a number of organ systems.

This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data p and their number n grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS-SVM decision function is asymptotically normal with means and covariances shown to depend explicitly on the derivatives of the kernel function. This provides improved understanding along with new insights into the internal workings of SVM-type methods for large datasets.

Human Activity Recognition (HAR) must currently face up to the challenge of rethinking analytics from the perspective of real-time operation, wherein biophysical sensing streams are efficiently intertwined at close vicinity to the point of sensing. As such, feature selection techniques, traditionally employed for off-line data processing, should be evaluated with respect to their ability to filter out redundant information in real-time.

Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts.

A novel two-stage age prediction approach with group-specific features is proposed in this paper. Aging process is captured through a highly discriminating feature representation that models shape, appearance, skin spots, and wrinkles. The two-stage method consists of a multi-class Support Vector Machine (SVM) to predict the age bracket while the final age prediction is carried out using Support Vector Regression (SVR). The novelty of our work is that the feature extraction is group-specific and can therefore be tailored to each age bracket in the specific age prediction step.

Through-the-wall radar imaging is an electromagnetic wave sensing technology capable of detecting targets behind walls, doors, and opaque obstacles. Identification of stationary targets is often achieved by first forming an image of the scene, and then segmenting and classifying the targets of interest. In order to provide prompt and reliable situational awareness, this paper proposes a radar signal classification approach that does not rely on image formation.