In this paper, we review 300 references on video retrieval, indicating when text-only
solutions are unsatisfactory and showing the promising alternatives which are in majority …

Learning-based video annotation is a promising approach to facilitating video retrieval and it
can avoid the intensive labor costs of pure manual annotation. But it frequently encounters …

In this paper we describe our TRECVID 2005 experiments. The UvA-MediaMill team
participated in four tasks. For the detection of camera work (runid: A CAM) we investigate the …

Visual categorization is important to manage large collections of digital images and video,
where textual metadata is often incomplete or simply unavailable. The bag-of-words model …

Concept classification is important to access visual information on the level of objects and
scene types. So far, intensity-based features have been widely used. To increase …

Concept detection is targeted at automatically labeling video content with semantic concepts
appearing in it, like objects, locations, or activities. While concept detectors have become …

Abstract The Microsoft SenseCam is a small lightweight wearable camera used to passively
capture photos and other sensor readings from a user's day-to-day activities. It captures on …

Due to the subjective nature of social tagging, measuring the relevance of social tags with
respect to the visual content is crucial for retrieving the increasing amounts of social …

Automatic video annotation is an important ingredient for semantic-level video browsing,
search and navigation. Much attention has been paid to this topic in recent years. These …

Our group within the University of Amsterdam participated in the large-scale visual concept
detection task of ImageCLEF 2009. Our experiments focus on increasing the robustness of …