Overview of MediaEval 2011 rich speech retrieval task and genre tagging task

The MediaEval 2011 Rich Speech Retrieval Tasks and Genre Tagging Tasks are two new tasks oered in MediaEval 2011 that are designed to explore the development of techniques for semi-professional user generated content (SPUG). They both use the same data set: the MediaEval 2010 Wild Wild Web Tagging Task (ME10WWW). The ME10WWW data set contains Creative Commons licensed video collected from blip.tv in 2009. It was created by the PetaMedia Network of Excellence (http://www.petamedia.eu) in order to test retrieval algorithms for video content as it occurs `in the wild' on the Internet and, in particular, for user contributed multimedia that is embedded within a social network. In this overview paper, we repeat the essential characteristics of the data set, describe the tasks and specify how they are evaluated

Blip10000: a social video dataset containing SPUG content for tagging and retrieval

The increasing amount of digital multimedia content available is inspiring potential new types of user interaction with video data. Users want to easilyfind the content by searching and browsing. For this reason, techniques are needed that allow automatic categorisation, searching the content and linking to related information. In this work, we present a dataset that contains comprehensive semi-professional user generated (SPUG) content, including audiovisual content, user-contributed metadata, automatic speech recognition transcripts, automatic shot boundary les, and social information for multiple `social levels'. We describe the principal characteristics of this dataset and present results that have been achieved on different tasks

Feature-based video key frame extraction for low quality video sequences

We present an approach to key frame extraction for structur-ing user generated videos on video sharing websites (e. g. YouTube). Our approach is intended to link existing image search engines to video data. User generated videos are, con-trary to professional material, unstructured, do not follow any fixed rule, and their camera work is poor. Furthermore, the coding quality is bad due to low resolution and high compres-sion. In a first step, we segment video sequences into shots by detecting gradual and abrupt cuts. Further, longer shots are segmented into subshots based on location and camera mo-tion features. One representative key frame is extracted per subshot using visual attention features, such as lighting, cam-era motion, face, and text appearance. These key frames are useful for indexing and for searching similar video sequences using MPEG-7 descriptors [1]. 1

Blip10000: A social video dataset containing SPUG content for tagging and retrieval

International audienceThe increasing amount of digital multimedia content available is inspiring potential new types of user interaction with video data. Users want to easily find the content by searching and browsing. For this reason, techniques are needed that allow automatic categorisation, searching the content and linking to related information. In this work, we present a dataset that contains comprehensive semi-professional user-generated (SPUG) content, including audiovisual content, user-contributed metadata, automatic speech recognition transcripts, automatic shot boundary files, and social information for multiple `social levels\'. We intend this Blip10000 dataset to be a useful resource for evaluating tagging techniques as well as retrieval techniques. We describe the principal characteristics of this dataset and present results that have been achieved on different tasks

Disease burden and risk profile in referred patients with moderate chronic kidney disease: composition of the German Chronic Kidney Disease (GCKD) cohort

Background A main challenge for targeting chronic kidney disease (CKD) is the heterogeneity of its causes, co-morbidities and outcomes. Patients under nephrological care represent an important reference population, but knowledge about their characteristics is limited. Methods We enrolled 5217 carefully phenotyped patients with moderate CKD [estimated glomerular filtration rate (eGFR) 30–60 mL/min per 1.73 m2 or overt proteinuria at higher eGFR] under routine care of nephrologists into the German Chronic Kidney Disease (GCKD) study, thereby establishing the currently worldwide largest CKD cohort. Results The cohort has 60% men, a mean age (±SD) of 60 ± 12 years, a mean eGFR of 47 ± 17 mL/min per 1.73 m2 and a median (IQR) urinary albumin/creatinine ratio of 51 (9–392) mg/g. Assessment of causes of CKD revealed a high degree of uncertainty, with the leading cause unknown in 20% and frequent suspicion of multifactorial pathogenesis. Thirty-five per cent of patients had diabetes, but only 15% were considered to have diabetic nephropathy. Cardiovascular disease prevalence was high (32%, excluding hypertension); prevalent risk factors included smoking (59% current or former smokers) and obesity (43% with BMI >30). Despite widespread use of anti-hypertensive medication, only 52% of the cohort had an office blood pressure <140/90 mmHg. Family histories for cardiovascular events (39%) and renal disease (28%) suggest familial aggregation. Conclusions Patients with moderate CKD under specialist care have a high disease burden. Improved diagnostic accuracy, rigorous management of risk factors and unravelling of the genetic predisposition may represent strategies for improving prognosis