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

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

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

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