Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Relevance Feedback Techniques for Color-based Image Retrieval
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
A Weighted Distance Approach to Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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In modern multimedia databases, objects can be represented by a large variety of feature representations. In order to employ all available information in a best possible way, a joint statement about object similarity must be derived. In this paper, we present a novel technique for multi-represented similarity estimation which is based on probability distributions modeling the connection between the distance value and object similarity. To tune these distribution functions to model the similarity in each representation, we propose a bootstrapping approach maximizing the agreement between the distributions. Thus, we capture the general notion of similarity which is implicitly given by the distance relationships in the available feature representations. Thus, our approach does not need any training examples. In our experimental evaluation, we demonstrate that our new approach offers superior precision and recall compared to standard similarity measures on a real world audio data set.