Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification.

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.

LogDet divergence-based metric learning with triplet constraints and its applications.

How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn, store, and evaluate Mahalanobis matrix efficiently. Besides, a dynamic triplets building strategy is proposed to build a feedback from the obtained Mahalanobis matrix to the triplet constraints, which can further improve the LDMLT algorithm. Furthermore, the proposed method is applied to various applications, including pattern recognition, facial expression recognition, and image retrieval. The results demonstrate the improved performance of the proposed approach.

A curve evolution approach for unsupervised segmentation of images with low depth of field.

In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.

The HIV epidemic in Yunnan Province, China, 1989-2007.

OBJECTIVE: To investigate the characteristics and trends in the HIV epidemic in Yunnan province, China, between 1989 and 2007. METHODS: Statistical analysis of serological data from voluntary testing and counseling sites, medical case reports, mass screenings, sentinel surveillance, and other sources. RESULTS: By 2007, a cumulative total of 57,325 cases of HIV infection were reported in Yunnan, and unsafe drug injection practices and unsafe sexual behaviors were identified as the dominant modes of transmission. HIV affects injecting drug users most, particularly in Jingpo, Dai, and Yi ethnicities, more than 40% in 7 counties. HIV prevalence rates among female sex workers (FSWs) increased from 0.5% in 1995 to 4.0% in 2007; among men who have sex with men, from 4.0% in 2005 to 13.2% in 2007; among male clients of FSWs, from 0% in 1995-1997 to 1.8% in 2007; among male sexually transmitted disease clinic attendees, from 0% in 1992 to 2.1% in 2007; among pregnant women from 0.16% in 1992 to 0.5% in 2007; and among blood donors, from 0.0075% in 1992 to 0.084% in 2007. CONCLUSIONS: The HIV epidemic in Yunnan has progressed to a concentrated epidemic. Future efforts must focus on not only groups at risk for primary infection (injecting drug users, men who have sex with men, and FSWs) but also on their low-risk sexual partners.