For a long time, it was difficult to automatically extract meanings from video shots, because, even for a particular meaning, shots are characterized by signifincantly different visual appearances, depending on camera techniques and shooting environments. One promising approach for this has been recently devised where a large amount of shots are statistically analyzed to cover diverse visual appearances for a meaning. Inspired by the significant performance improvement, concept-based video retrieval receives much research attention. Here, concepts are abstracted names of meanings that humans can perceive from shots, like objects, actions, events, and scenes. For each concept, a detector is built in advance by analyzing a large amount of shots. Then, given a query, shots are retrieved based on concept detection results. Since each detector can detect a concept robustly to diverse visual appearances, effective retrieval can be achieved using concept detection results as “intermediate” features. However, despite the recent improvement, it is still difficult to accurately detect any kind of concept. In addition, shots can be taken by arbitrary camera techniques and in arbitrary shooting environments, which unboundedly increases the diversity of visual appearances. Thus, it cannot be expected to detect concepts with an accuracy of 100 %. This chapter explores how to utilize such uncertain detection results to improve concept-based video retrieval.
CITATION STYLE
Shirahama, K. A., Kumabuchi, K., Grzegorzek, M., & Uehara, K. (2015). Video retrieval based on uncertain concept detection using dempster–shafer theory. In Multimedia Data Mining and Analytics: Disruptive Innovation (pp. 269–294). Springer International Publishing. https://doi.org/10.1007/978-3-319-14998-1_12
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