Social networks such as Facebook, video and photo hosting sites like YouTube and Flickr, file-hosting service sites like Dropbox, business review sites like Yelp, and e-commerce sites like eBay, have become more and more popular, allowing people to easily upload, share and annotate massive amounts of images and videos. Web-scale visual search thus has recently become a very active inter-disciplinary area, involving computer vision, multimedia, machine learning, information retrieval, and data mining. Most content based image/video processing, such as retrieval or classification, are performed on image "features", which are much more compact relative to the raw data. Yet the volumes of such image features still can be huge due to the amount and the dimensionality.

Hence, my research is focused on addressing some essential aspects of the problem, including scalable clustering, approximate nearest neighbor search, and fast bin matching for near-duplicate image/video detection/mining. The experimental results have shown the proposed methods are comparable or better than existing solutions in terms of trade-off among search time, memory usage, database storage, and search accuracy.