Résumé (EN)

During last years, local image descriptors have received much attention because of their efficiency for several computer vision tasks such as image retrieval, image comparison, features matching for 3D reconstruction... Recent surveys have shown that Scale Invariant Features Transform (SIFT) vectors are the most efficient for several criteria. In this article, we use these descriptors to analyze how a large input image can be decomposed by small template images contained in a database. Affine transformations from database images onto the input image are found as described in [16]. The large image is thus covered by small patches like a jigsaw puzzle. We introduce a filtering step to ensure that found images do not overlap themselves when warped on the input image. A typical new application is to retrieve which products are proposed on a supermarket shelf. This is achieved using only a large picture of the shelf and a database of all products available in the supermarket. Because the database can be large and the analysis should ideally be done in a few seconds, we compare the performances of two state of the art algorithms to search SIFT correspondences: Best-Bin-First algorithm on Kd-Tree and Locality Sensitive Hashing. We also introduce a modification in the LSH algorithm to adapt it to SIFT vectors.