Near duplicate image identification with patially Aligned Pyramid Matching
A new framework, termed spatially aligned pyramid matching, is proposed for near duplicate image identification. The proposed method robustly handles spatial shifts as well as scale changes. Images are divided into both overlapped and non-overlapped blocks over multiple levels. In the first matching stage, pairwise distances between blocks from the examined image pair are computed using SIFT features and Earth Moverpsilas distance (EMD). In the second stage, multiple alignment hypotheses that consider piecewise spatial shifts and scale variation are postulated and resolved using integer-flow EMD. Two application scenarios are addressed - retrieval ranking and binary classification. For retrieval ranking, a pyramid-based scheme is constructed to fuse matching results from different partition levels. For binary classification, a novel generalized neighborhood component analysis method is formulated that can be effectively used in tandem with SVMs to select the most critical matching components. The proposed methods are shown to clearly outperform existing methods through extensive testing on the Columbia near duplicate image database and another new dataset.