Polyphonic Acoustic Event Detection (AED) is a challenging task as the sounds are mixed with the signals from different events, and the features extracted from the mixture do not match well with features calculated from sounds in isolation, leading to suboptimal AED performance. In this paper, we propose a supervised β-VAE model for AED, which adds a novel event-specific disentangling loss in the objective function of disentangled learning. By incorporating either latent factor blocks or latent attention in disentangling, supervised β-VAE learns a set of discriminative features for each event. Extensive experiments on benchmark datasets show that our approach outperforms the current state-of-the-arts (top-1 performers in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 AED challenge). Supervised β-VAE has great success in challenging AED tasks with a large variety of events and imbalanced data.

Similarity search is a fundamental problem in network analysis and can be applied in many applications, such as collaborator recommendation in coauthor networks, friend recommendation in social networks, and relation prediction in medical information networks. In this article, we propose a sampling-based method using random paths to estimate the similarities based on both common neighbors and structural contexts efficiently in very large homogeneous or heterogeneous information networks. We give a theoretical guarantee that the sampling size depends on the error-bound ε, the confidence level (1-δ), and the path length T of each random walk. We perform an extensive empirical study on a Tencent microblogging network of 1,000,000,000 edges. We show that our algorithm can return top- k similar vertices for any vertex in a network 300× faster than the state-of-the-art methods. We develop a prototype system of recommending similar authors to demonstrate the effectiveness of our method.

Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors and structural contexts have received much attention. However, both categories of methods are difficult to scale up to handle large networks (with billions of nodes). In this paper, we propose a sampling method that provably and accurately estimates the similarity between vertices. The algorithm is based on a novel idea of random path. Specifically, given a network, we perform R random walks, each starting from a randomly picked vertex and walking T steps. Theoretically, the algorithm guarantees that the sampling size R = O(2ε-2 log2 T) depends on the error-bound ε, the confidence level (1 -- δ), and the path length T of each random walk.

Accurate point matching is a critical and challenging process in feature-based image registration. In this paper, a simple and robust feature point matching algorithm, called Restricted Spatial Order Constraints (RSOC), is proposed to remove outliers for registering aerial images with monotonous backgrounds, similar patterns, low overlapping areas, and large affine transformation. In RSOC, both local structure and global information are considered. Based on adjacent spatial order, an affine invariant descriptor is defined, and point matching is formulated as an optimization problem. A graph matching method is used to solve it and yields two matched graphs with a minimum global transformation error. In order to eliminate dubious matches, a filtering strategy is designed. The strategy integrates two-way spatial order constraints and two decision criteria restrictions, i.e., the stability and accuracy of transformation error. Twenty-nine pairs of optical and Synthetic Aperture Radar (SAR) aerial images are utilized to evaluate the performance. Compared with RANdom SAmple Consensus (RANSAC), Graph Transformation Matching (GTM), and Spatial Order Constraints (SOC), RSOC obtained the highest precision and stability.

Edge detection is a crucial approach for the location and acreage calculation of oil slick when oil spills on the sea. In this paper, in view of intensity inhomogeneity, high noise, and blurring of oil slick infrared (IR) aerial images, a novel algorithm is proposed to detect the edges of oil slick IR aerial images. In the proposed algorithm, we define an energy function model combining a region-scalable-fitting concept and a global minimization active contour (GMAC) model. The proposed novel algorithm avoids the existence of local minima and meanwhile deals with the intensity inhomogeneity, noise, and weak edge boundaries exiting in oil spill IR images. In the process of the active contour evolving toward object boundaries and numerical minimization, a dual formulation is used for overcoming drawbacks of the usual level set and gradient descent method so that the process of minimization can be much easier and our algorithm is independent of the initial position of the contour. Using the proposed algorithm, we can gain continuous and closed edges of oil slick IR aerial images. The experiment results have shown that the proposed algorithm outperforms conventional edge detection methods and other algorithms in terms of the efficiency and accuracy. In addition, the proposed algorithm is extended to synthetic-aperture-radar oil slick images, and satisfactory results of edge extraction can be obtained as well.