Clustering of scene repeats for essential rushes preview

This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques on shot key-frames are presented and compared in order to choose the best representative segments to enter the preview. Experiments performed on TRECVID data are evaluated by computing the mutual information between the obtained results and a manually annotated ground-truth

Sports video: Fine-grained action detection and classification of table tennis strokes from videos for MediaEval 2021

This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

COST292 experimental framework for TRECVID 2008

In this paper, we give an overview of the four tasks submitted to TRECVID 2008 by COST292. The high-level feature extraction framework comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a multi-modal classifier based on SVMs and several descriptors. The third system uses three image classifiers based on ant colony optimisation, particle swarm optimisation and a multi-objective learning algorithm. The fourth system uses a Gaussian model for singing detection and a person detection algorithm. The search task is based on an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. The rushes task submission is based on a spectral clustering approach for removing similar scenes based on eigenvalues of frame similarity matrix and and a redundancy removal strategy which depends on semantic features extraction such as camera motion and faces. Finally, the submission to the copy detection task is conducted by two different systems. The first system consists of a video module and an audio module. The second system is based on mid-level features that are related to the temporal structure of videos

Sport Task: Fine grained action detection and classification of table tennis strokes from videos for MediaEval 2022

Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this task aims at detecting and classifying subtle movements from sport videos. We focus on recordings of table tennis matches. Conducted since 2019, this task provides a classification challenge from untrimmed videos recorded under natural conditions with known temporal boundaries for each stroke. Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos. This year, the training, validation, and test sets are enhanced to ensure that all strokes are represented in each dataset. The dataset is now similar to the one used in [1, 2]. This research is intended to build tools for coaches and athletes who want to further evaluate their sport performances

Prolonged Survival in a Patient with Neuroendocrine Tumor of the Cecum and Diffuse Peritoneal Carcinomatosis

Peritoneal carcinomatosis is a well-known factor of poor prognosis in patients with digestive adenocarcinomas. Peritoneal dissemination may also occur in midgut well-differentiated neuroendocrine tumors, but its influence on survival is ill-defined. We report here the history of a 64-year-old woman who had a neuroendocrine tumor of the cecum with multiple synchronous metastases in the liver and diffuse peritoneal carcinomatosis. She underwent surgical resection of the primary tumor and cytoreduction of liver metastases, and received subsequently chemotherapy and somatostatin analogs. In spite of the widespread extension of the disease, she survived for 13 years and died from a carcinoid heart disease. We discuss the natural history and prognostic factors in patients with midgut well-differentiated neuroendocrine tumors, with a focus on the impact of the peritoneal carcinomatosis

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Urea levels and cardiovascular disease in patients with chronic kidney disease

Background: Elevated serum urea levels are common in moderate-to-advanced chronic kidney disease (CKD). Several studies have shown that urea is a direct and indirect uraemic toxin, especially with regard to cardiovascular disease. We sought to determine whether serum urea levels are associated with adverse cardiovascular events and death before renal replacement therapy (RRT) in patients with CKD. Methods: CKD-REIN is a prospective cohort of CKD nephrology outpatients not receiving maintenance dialysis. The 2507 patients included in the analysis were divided into three groups according to the baseline serum urea level (T1 <10.5, T2 10.5-15.1 and T3 ≥15.1 mmol/L). Cox proportional hazard models were used to estimate hazard ratios (HRs) for first atheromatous or non-atheromatous cardiovascular (CV) events and all-cause mortality before RRT. The models were adjusted for baseline comorbidities, laboratory data and medications. Findings: Of the 2507 included patients median [interquartile range (IQR)] age: 69 [61-77]; mean (standard deviation) estimated glomerular filtration rate (eGFR) 33.5 (11.6) mL/min/1.73 m², 54% had a history of cardiovascular disease. After multiple adjustments for CV risk factors (including eGFR), patients in T3 had a higher risk of atheromatous and non-atheromatous CV events than patient in T1 (n events = 451, HR [95% CI]: 1.93 [1.39; 2.69]). The adjusted HRs for death before RRT (n events = 407) were 1.31 [0.97; 1.76] and 1.73 [1.22; 2.45] for patients T2 and those in T3, respectively. Interpretation: Our data suggested that urea is a predictor of CV outcomes beyond CV risk factors including eGFR. © 2022 The Author(s). Published by Oxford University Press on behalf of the ERA