Orientation information is critical to the accuracy of ear recognition systems. In this paper, a new feature extraction approach is investigated for ear recognition by using orientation information of Gabor wavelets. The proposed Gabor orientation feature can not only avoid too much redundancy in conventional Gabor feature but also tend to extract more precise orientation information of the ear...

Unsupervised methods have proven effective for discriminative tasks in a singlemodality scenario. In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities. The framework can model relationships at a higher level by forcing the shared sparse representation. In particular, we propose the use of joint dictionary l...

I. INTRODUCTION Traditional spectral imaging sensors entail the acquisition of high-dimensional data that is used for the discrimination of objects and features in a scene. Recently, a novel architecture known as coded aperture snapshot spectral imaging (CASSI) system has been proposed for the acquisition of compressive spectral image data of a scene with just a few coded focal plane array (FPA...

We introduce a new large-scale discriminative learning algorithm for machine translation that is capable of learning parameters in models with extremely sparse features. To ensure their reliable estimation and to prevent overfitting, we use a two-phase learning algorithm. First, the contribution of individual sparse features is estimated using large amounts of parallel data. Second, a small dev...

Fine-grained action segmentation and recognition is an important yet challenging task. Given a long, untrimmed sequence of kinematic data, the task is to classify the action at each time frame and segment the time series into the correct sequence of actions. In this paper, we propose a novel framework that combines a temporal Conditional Random Field (CRF) model with a powerful frame-level repr...

Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, compression or classification. The sparsity of the representation very much depends on how well the dictionary is adapted to the data at hand. In this paper, we propose a method for learning structured multilevel diction...