MMIS at ImageCLEF 2009: Non-parametric Density Estimation Algorithms

This paper presents the work of the MMIS group done at ImageCLEF 2009. We submitted five different runs to the Photo Annotation task. These runs were based on two non-parametric density estimation models. The first one evaluates a set of visual features and proposes a better, weighted set of features. The second approach uses keyword correlation to compute semantic similarity measures using several knowledge sources. The knowledge sources used are, the training set of the collection, Google Web search engine, WordNet and Wikipedia. Evaluation of results is done under two different metrics, one based on ROC curves and the other in a hierarchical measure proposed by the organisers. Our results are quite encouraging; under the first metric our best run was located between the median and the top quartile and under the second metric our best run was between the first quartile and the median.