T o provide e f i c i e n t search and retrieval of video data f r o m large archives, we need t o model video data appropriately. In this paper we propose a video data model an,d describe the design and implementa t ion of the annotation-based video retrieval s y s t e m VIRON (Video I n f o r m a t i o n Retr ieval On Nota t ion) based o n the proposed model . T h i s model provides the mecha...

How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such representations of even possible? To address questions enable research towards general affective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is large...

The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval [5, 9] and computer vision applications [6, 8, 13]. In order to support the users during the image and video annotation process, several software tools have been developed to provide them with a graphical environment which helps drawing object contours, handling tracking informatio...

The shortage of emotion-annotated video datasets suitable for training and validating machine learning models facial expression-based emotion recognition stems primarily from the significant effort cost required manual annotation. In this paper, we present AffecTube as a comprehensive solution that leverages crowdsourcing to annotate videos directly on YouTube platform, resulting in ready-to-us...

In this paper, we propose an efficient method for extracting scene tags from online video annotation (e.g., comments about video scenes). To evaluate this method by applying extracted information to video scene retrieval, we have developed a video scene retrieval system based on scene tags (i.e., tags associated with video scenes). We have also developed a tag selection system that enables onli...

The availability of databases of images labeled with keywords is necessary for developing and evaluating image annotation models. Dataset collection is however a costly and time consuming task. In this paper we exploit the vast resource of images available on the web. We create a database of pictures that are naturally embedded into news articles and propose to use their captions as a proxy for...