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Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Graph-based representations play a key role in machine learning. The fundamental step in these representations is the association of a graph structure to a dataset. In this paper, we propose a method that finds a block sparse representation of the data by associating a graph, whose Laplacian matrix admits the sparsifying dictionary as its eigenvectors.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. 

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

The IEEE Transactions on Image Processing covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications.

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

Zero-shot learning (ZSL) for visual recognition aims to accurately recognize the objects of unseen classes through mapping the visual feature to an embedding space spanned by class semantic information. However, the semantic gap across visual features and their underlying semantics is still a big obstacle in ZSL. Conventional ZSL methods construct that the mapping typically focus on the original visual features that are independent of the ZSL tasks, thus degrading the prediction performance.

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept and 2) how to annotate an image with the optimal number of class labels.

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios.

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

A novel scheme of edge detection based on the physical law of diffusion is presented in this paper. Though the most current studies are using data based methods such as deep neural networks, these methods on machine learning need big data of labeled ground truth as well as a large amount of resources for training. On the other hand, the widely used traditional methods are based on the gradient of the grayscale or color of images with using different sorts of mathematical tools to accomplish the mission.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

During the Christmas break, I had a mundane problem to solve. Most PCs today no longer come with disk drives. This was problematic for me since, like for many of you, my family videos are stored on CDs and DVDs.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In this column of IEEE Signal Processing Magazine, 46 IEEE Signal Processing Society (SPS) members are recognized as IEEE Fellows and award recipients are announced.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Industrial control systems (ICSs) manage and monitor critical civil or military infrastructure, such as water treatment facilities, power plants, electricity grids, transportation systems, oil and gas refineries, and health care.