Evaluation of visual object retrieval datasets

Recently visual object retrieval has being widely studied for its vast application prospect, and the progress is usually tracked by the performance achieved on benchmark datasets. Therefore, in order to faithfully evaluate object retrieval algorithms, the quality of datasets must be ensured by some means. In this paper, we propose a method to evaluate the quality of benchmarking visual object retrieval on two highly cited object retrieval datasets, the Oxford datasets and TrecVid instance search datasets. Our evaluation method leverages the essential differences between object retrieval and other similar image search, and digs out some unrevealed and rather interesting features from those datasets. To the best of our knowledge, this research has never been touched before. Our method is believed to be beneficial to dataset collection and algorithm evaluation of object retrieval. More importantly, we hope this work can attract more attentions on this topic in the community.