Emmanuel Dellandréa

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Research in affective computing requires ground truth data for training and benchmarking computational models for machine-based emotion understanding. In this paper, we propose a large video database, namely LIRIS-ACCEDE, for affective content analysis and related applications, including video indexing, summarization or browsing. In contrast to existing(More)
This paper provides a description of the MediaEval 2015 “Affective Impact of Movies Task”, which is running for the fifth year, previously under the name “Violent Scenes Detection”. In this year’s task, participants are expected to create systems that automatically detect video content that depicts violence, or predict the affective impact that video(More)
We describe the LIRIS human activities dataset, the dataset used for the ICPR 2012 human activities recognition and localization competition. In contrast to previous competitions and existing datasets, the tasks focus on complex human behavior involving several people in the video at the same time, on actions involving several interacting people and on(More)
Evaluating the performance of computer vision algorithms is classically done by reporting classification error or accuracy, if the problem at hand is the classification of an object in an image, the recognition of an activity in a video or the categorization and labeling of the image or video. If in addition the detection of an item in an image or a video,(More)
Speech emotion as anger, boredom, fear, gladness, etc. is high semantic information and its automatic analysis may have many applications such as smart human-computer interactions or multimedia indexing. Main difficulties for an efficient speech emotion classification reside in complex emotional class borders leading to necessity of appropriate audio(More)
To contribute to the need for emotional databases and affective tagging, the LIRIS-ACCEDE is proposed in this paper. LIRIS-ACCEDE is an Annotated Creative Commons Emotional DatabasE composed of 9800 video clips extracted from 160 movies shared under Creative Commons licenses. It allows to make this database publicly available without copyright issues. The(More)
Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep(More)
Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark(More)
The ImageCLEF 2015 Scalable Image Annotation, Localization and Sentence Generation task was the fourth edition of a challenge aimed at developing more scalable image annotation systems. In particular this year the focus of the three subtasks available to participants had the goal to develop techniques to allow computers to reliably describe images, localize(More)
This paper presents an overview of the ImageCLEF 2016 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2016. ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in(More)