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| Content Provider | ACM Digital Library |
|---|---|
| Author | de Vries, Arjen Aly, Robin Hiemstra, Djoerd |
| Abstract | Describing shots through the occurrence of semantic concepts is the first step towards modeling the content of a video semantically. An important challenge is to automatically select the right concepts for a given information need. For example, systems should be able to decide whether the concept "Outdoor" should be included into a search for "Street Basketball". In this paper we provide an innovative method to automatically select concepts for an information need. To achieve this, we provide an estimation for the occurrence probability of a concept in relevant shots, which helps us to quantify the helpfulness of a concept. Our method reuses existing training data which is annotated with concept occurrences to build a text collection. Searching in this collection with a text retrieval system and knowing about the concept occurrences allows us to come up with a good estimate for this probability. We evaluate our method against a concept selection benchmark and search runs on both the TRECVID 2005 and 2007 collections. These experiments show that the estimation consistently improves retrieval effectiveness. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781605584805 |
| DOI | 10.1145/1646396.1646448 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-07-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Video information retrieval Semantic concept selection |
| Content Type | Text |
| Resource Type | Article |
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