In content based video retrieval videos are often indexed with semantic labels (concepts) using pre-trained classifiers. These pre-trained classifiers (concept detectors), are not perfect, and thus the labels are noisy. Additionally, the amount of pre-trained classifiers is limited. Often automatic methods cannot represent the query adequately in terms of the concepts available. This problem is also apparent in the retrieval of events, such as bike trick or birthday party. Our solution is to obtain user feedback. This user feedback can be provided on two levels: concept level and video level. We introduce the method Adaptive Relevance Feedback (ARF) on video level feedback. ARF is based on the classical Rocchio relevance feedback method from Information Retrieval. Furthermore, we explore methods on concept level feedback, such as the re-weighting and Query Point Modification (QPM) methods as well as a method that changes the semantic space the concepts are represented in. Methods on both concept level and video level are evaluated on the international benchmark TRECVID Multimedia Event Detection (MED) and compared to state of the art methods. Results show that relevance feedback on both concept and video level improves performance compared to using no relevance feedback; relevance feedback on video level obtains higher performance compared to relevance feedback on concept level; our proposed ARF method on video level outperforms a state of the art k-NN method, all methods on concept level and even manually selected concepts.

No abstract

No abstract