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 re-uses 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.
|Title of host publication||Proceedings of the 8th ACM International Conference on Image and Video Retrieval (CIVR '09)|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||8|
|Publication status||Published - Jun 2009|
Aly, R., Hiemstra, D., & de Vries, A. P. (2009). Reusing Annotation Labor for Concept Selection. In Proceedings of the 8th ACM International Conference on Image and Video Retrieval (CIVR '09) (pp. 44:1-44:8). [10.1145/1646396.1646448] New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/1646396.1646448