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ABSTRACT: This study investigated the impact of metabolic syndrome on the development of cardio-cerebral vascular (CVD) events in a pre-hypertensive population.
The data used in this prospective study was derived from the Kailuan study cohort (n = 101 510). Prehypertension was diagnosed in 29 968 (mean age: 50 ± 9 years and 23 744 males) individuals by the JNC VII criteria and these subjects were further classified into metabolic syndrome positive (MS+, n = 3447) and MS negative (MS-, n = 26 521) groups according to the modified 2004 Chinese Diabetes Society criteria. Subjects were followed up for 38 - 53 (mean 47 ± 5) months and first-ever CVD events were recorded. Baseline anthropometric and laboratory features were obtained by physical examination from June 2006 to October 2007 and the last follow-up day was December 31, 2010. Multivariable Cox proportional hazards regression models were used to analyze the risk factors of first-ever CVD events.
There were 354 CVD events during follow up. The incidences of CVD events (1.80% vs. 1.28%) and cerebral infarction (1.10% vs. 0.57%) were significantly higher in the MS+ group than in the MS- group (all P < 0.05). After adjustment for other established CVD risk factors, the hazards ratio was 1.45 (95%CI: 1.10 - 1.92) for total CVD events and 1.84 (95%CI: 1.27 - 2.67) for cerebral infarction events in MS+ group.
In this cohort, metabolic syndrome is linked with increased risk for CVD events.
Zhonghua xin xue guan bing za zhi [Chinese journal of cardiovascular diseases] 05/2012; 40(5):397-401.
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Pattern Recognition Letters. 01/2012; 33:371-372.
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18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011
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ABSTRACT: We propose a higher-level visual representation, visual synset, for object-based image retrieval beyond visual appearances.
The proposed visual representation improves the traditional part-based bag-of-words image representation, in two aspects.
First, the approach strengthens the discrimination power of visual words by constructing an intermediate descriptor, visual
phrase, from frequently co-occurring visual word-set. Second, to bridge the visual appearance difference or to achieve better
intra-class invariance power, the approach clusters visual words and phrases into visual synset, based on their class probability
distribution. The rationale is that the distribution of visual word or phrase tends to peak around its belonging object classes.
The testing on Caltech-256 data set shows that the visual synset can partially bridge visual differences of images of the
same class and deliver satisfactory retrieval of relevant images with different visual appearances.
The Visual Computer 12/2008; 25(1):13-23. · 0.58 Impact Factor
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Multimedia Syst. 01/2008; 14:179-193.
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Advances in Multimedia Modeling, 14th International Multimedia Modeling Conference, MMM 2008, Kyoto, Japan, January 9-11, 2008, Proceedings; 01/2008
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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA; 01/2008
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Proceedings of the 7th ACM International Conference on Image and Video Retrieval, CIVR 2008, Niagara Falls, Canada, July 7-9, 2008; 01/2008
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ABSTRACT: Widely used in the evaluation of retrieval systems, the pooling method collects top ranked images from submitted retrieval systems resulting in possibly a very large pool of images. Inevitably, the pool may contain outliers. Human experts then manually annotate the relevance of them to create a ground truth for evaluation. Studies show that this annotation is time-consuming, tedious and inconsistent. To reduce human workload, this paper introduces an automatic method to detect outliers. Different from traditional detection methods using unsupervised techniques only, we utilize both supervised and unsupervised techniques sequentially as both positive and negative examples are (partially) available in this context. Specifically, support vector machines (SVMs) and fuzzy c-means clustering are used to predict data relevance and "outlierness". Performance improvements using our method after outlier removal have been validated on the medical image retrieval task in ImageCLEF 2004
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on; 05/2007 · 4.63 Impact Factor
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TOMCCAP. 01/2007; 3.
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IEEE Transactions on Multimedia. 01/2007; 9:576-588.
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Multimedia Tools Appl. 01/2007; 34:85-105.
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2007, April 15-20, 2007, Honolulu, Hawaii, USA; 01/2007
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Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007, July 2-5, 2007, Beijing, China; 01/2007
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Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, Amsterdam, The Netherlands, July 9-11, 2007; 01/2007
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ABSTRACT: There have been many features developed for images, like Blob, image patches, Gabor filters, etc. But generally the calculation
cost is too high. When facing a large image database, their responding speed can hardly satisfy users’ demand in real time,
especially for online users. So we developed a new image feature based on a new region division method of images, and named
it as ‘stripe’. As proved by the applications in ImageCLEF’s medical subtasks, stripe is much faster at the calculation speed
compared with other features. And its influence to the system performance is also interesting: a little higher than the best
result in ImageCLEF 2004 medical retrieval task (Mean Average Precision — MAP: 44.95% vs. 44.69%), which uses Gabor filters;
and much better than Blob and low-resolution map in ImageCLEF 2006 medical annotation task (classification correctness rate:
75.5% vs. 58.5% & 75.1%).
KeywordsStripe-image feature-image retrieval-image annotation
10/2006: pages 489-496;
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ABSTRACT: In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: principle component analysis (PCA) and supporting vector machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the middle-level blob feature and low-level features (down-scaled images and their texture maps) achieved the highest recognition accuracy. Using the 9000 given training images from ImageCLEFOS, our proposed method has achieved a recognition rate of 88.9% in a simulation experiment. And according to the evaluation result from the ImageCLEFOS organizer, our method has achieved a recognition rate of 82% over its 1000 testing images
Multimedia and Expo, 2006 IEEE International Conference on; 08/2006
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ABSTRACT: Motion characterization plays a critical role in video indexing. An effective way of characterizing camera motion facilitates the video representation, indexing and retrieval tasks. This paper describes a novel nonparametric motion representation to achieve an effective and robust recognition of parts of the video in which camera is static, or panning, or tilting, or zooming, etc. This representation employs the mean shift filtering and the vector histograms to produce a compact description of a motion field. The basic idea is to perform spatio-temporal mode-seeking in the motion feature space and use the histograms-based spatial distributions of dominant motion modes to represent a motion field. Unlike most existing approaches, which focus on the estimation of a parametric motion model from a dense optical flow field (OFF) or a block matching-based motion vector field (MVF), the proposed method combines the motion representation and machine learning techniques (e.g., support vector machines) to perform camera motion analysis from the classification point of view. The main motivation lies in the impossibility of uniformly securing a proper parametric assumption in a wide range of video scenarios. The diverse camera shot sizes and frequent occurrences of bad OFF/MVF necessitates a learning mechanism, which can not only capture the domain-independent parametric constraints, but also acquire the domain-dependent knowledge to tolerate the influence of bad OFF/MVF. In order to improve performance, we can use this learning-based method to train enhanced classifiers aiming at a certain context (i.e., shot size, neighbor OFF/MVFs, and video genre). Other visual cues (e.g., dominant color) can also be incorporated for further motion analysis. Our main aim is to use a generic feature space analysis method to explore a flexible OFF/MVF representation in a nonparametric technique, which could be fed into a learning framework to robustly capture the global motion by incorporating the context information. Results on videos with various types of content (23 191 MVFs culled from MPEG-7 dataset, and 20 000 MVFs culled from broadcast tennis, soccer, and basketball videos) are reported to validate the proposed approach.
IEEE Transactions on Multimedia 05/2006; · 1.93 Impact Factor
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ABSTRACT: Content-based video analysis calls for efficient and flexible video representation. In this paper, a novel multi-resolution
video representation (MRVR) scheme is proposed and realized by performing the kernel principal component analysis (KPCA) on
the low-level visual features extracted from a video sequence. By simply changing the kernel parameters or the dimensionality
of the subspace, this scheme can represent video content from coarser to finer levels in the subspace, according to its intrinsic
structure. An application of keyframe extraction is investigated to show the advantages of this representation scheme. Furthermore,
based on this scheme, a two-level video summarization approach is proposed to represent long video sequences. The experimental
results of both short and long video sequences have demonstrated the effectiveness and flexibility of the proposed video representation
scheme.
The Visual Computer 04/2006; 22(5):357-370. · 0.58 Impact Factor
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ABSTRACT: The ability to automatically identify and manage interesting segments from video documents is important and necessary. This paper presented a system that is able to generate personalized text and video summaries of soccer game. Our experimental results are promising. The proposed system is expected to bring impact on broadcasting industry.
Consumer Electronics, 2006. ICCE '06. 2006 Digest of Technical Papers. International Conference on; 02/2006
