A study into annotation ranking metrics in geo-tagged image corpora
Hughes, Mark, Jones, Gareth J.F.ORCID: 0000-0003-2923-8365 and O'Connor, Noel E.ORCID: 0000-0002-4033-9135
(2012)
A study into annotation ranking metrics in geo-tagged image corpora.
In: 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012), 24-25 Oct 2012, Copenhagen, Denmark.
Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf
to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task.
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Workshop
Refereed:
Yes
Uncontrolled Keywords:
Image Annotation; Landmark Recognition; Tag Relevance