Feature classification for representative photo selection
Wei-Ta Chu, Chia‐Hung Lin, Jen-Yu Yu · 2009
This paper points out that different local feature points provide different impacts to near-duplicate detection and related applications. Aiming to automatic representative photo selection, we develop three feature classification methods, i.e., point-based, region-based, and pLSA-based classification, to differentiate local feature points described by SIFT descriptors. We investigate the performance of these classification methods, and discuss how they influence near-duplicate detection and extended applications. Experiments show that, with effective feature classification, more accurate representative selection results can be achieved.