Optimized SIFT Feature Matching for Image Retrieval
Applying SIFT features for retrieval of visual data not only requires proper settings for the descriptor extraction but also needs well selected parameters for comparing these descriptors. Most researchers simply apply the standard values of the parameters without an adequate analysis of the parameters themselves. In this paper, we question the standard parameter settings and investigate the influence of the impor- tant comparison parameters. Based on the analysis on diverse data sets using different interest point detectors, we finally present an optimized combination of matching parameters which outperforms the standard values. We observe that two major parameters, i.e., distmax and ra- tiomax seem to have similar outcomes on different datasets of diverse nature for the application of scene retrieval. Thus, this paper shows that there is an almost global setting for these two parameters for local fea- ture matching. The outcomes of this work can also apply to other tasks like video analysis and object retrieval.