Ratio Voting: A New Voting Strategy for Large-Scale Image Retrieval

We propose a new voting strategy referred to as ratio voting to improve bag-of-visual words-based image retrieval. It limits the number of votes in proportion to the number of features in visual words, while conventional schemes use (estimated) distances or rank information as a filtering criterion. Ratio voting realizes adaptive thresholding that captures the density of feature vectors. In experiments, we adopt two different distance estimation methods in the post-filtering step and show that ratio voting achieves a considerable improvement in spite of its simplicity in both cases. Furthermore, we perform exhaustive experiments in combining ratio voting with multiple assignment approaches and show that choosing a multiple assignment approach also has a remarkable impact on accuracy.