Targeting diversity in photographic retrieval task with commonsense knowledge

Image search engines have a very limited usefulness since it is still difficult to provide different users with what they are searching for. This is because most research efforts to date have only been concentrating on relevancy rather than diversity which is also a quite important factor, given that the search engine knows nothing about the user's context. In this paper, we describe our approach for ImageCLEF 2008 photographic retrieval task. The novelty of our technique is the use of AnalogySpace [3], the reasoning technique over commonsense knowledge for document and query expansion, which aims to increase the diversity of the results. Our proposed technique combines AnalogySpace mapping with other two mappings namely, location and full-text. We then re-rank the resulting images from the mapping by trying to eliminate duplicate and near duplicate results in the top 20. We present our preliminary experiments and the results conducted using the IAPR TC-12 photographic collection with 20,000 natural still photographs. The results show that our integrated method with AnalogySpace yields slightly better performance in terms of cluster recall and the number of relevant photographs retrieved. We finally identify the weakness in our approach and ways on how the system could be optimized and improved.