In this paper we describe a multi-strategy approach to improving semantic extraction from news video. Experiments show the value of careful parameter tuning, exploiting multiple fe...
Alexander G. Hauptmann, Ming-yu Chen, Michael G. C...
According to some current thinking, a very large number of semantic concepts could provide researcher a novel way to characterize video and be utilized for video retrieval and und...
We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize ...
Semantic understanding of multimedia content is critical in enabling effective access to all forms of digital media data. By making large media repositories searchable, semantic ...
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. A major obstacle to this is the insufficiency o...
In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...
Temporal consistency is ubiquitous in video data, where temporally adjacent video shots usually share similar visual and semantic content. This paper presents a thorough study of ...
A novel framework is introduced for visual event detection. Visual events are viewed as stochastic temporal processes in the semantic concept space. In this concept-centered appro...
Image retrieval has been widely used in many fields of science and engineering. The semantic concept of user interest is obtained by a learning process. Traditional techniques oft...
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. These semantic concepts do not exist in isolatio...