Learning-Based Incremental Creation of Web Image Databases

Manually creating an object category dataset requires a lot of hard work and wastes a large amount of time. Having an automatic means for collecting images that represent different objects is crucial for the scalable and practical expansion of these datasets. In this work, a methodology to automatically re-rank the images returned from a web search engine is proposed to improve the precision of the retrieved results. The proposed system works in an incremental way to improve the learnt object model and achieve better precision in each iteration. Images along with their meta data are ranked, then re-filtered based on their textual and visual features to produce a robust set of seed images. These images are used in learning weighted distances between the images which are used to incrementally expand the collected dataset. Using our method, we automatically gather very large object category datasets. We also improve the image ranking performance of the retrieved results over web search engines and other batch methods.