PUBLICATIONS

We propose an online image search engine based on local image features (key points), which runs fully on GPUs. State-of-the-art visual image retrieval techniques are based on bag-of-visual-words (BoV) model, which is an analogy for text-based search. In BoV, each key point is rounded off to the nearest visual word. On the other hand in this work, thanks to the vector computation power of GPUs, we utilize real values of key point descriptors. We match key points in two steps. The idea in the first step is similar to visual word matching in BoV. In the second step, we do matching in key point level. By keeping identities of each key point, closest key points are accurately retrieved in real-time. Image search has different characteristics than textual search. We implement one-to-one key point matching, which is more natural for images. Our experiments reveal 265 times speed up for offline index generation, 104 times speedup for online index search and 20.5 times speedup for online key point matching time, when compared to the CPU implementation. Our proposed key point-matching-based search improves accuracy of BoV by 9.5%.