Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
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Recently we have proposed a new indexing method for high-dimensional data, the PvS-index. It provides fast query processing in constant time and is well suited for doing similarity search in Image Retrieval Systems using local descriptors. It is based on projecting data points onto random lines and uses this information to segment them into appropriately sized buckets, which can be read in just one I/O operation. After this preprocessing step the search queries just three buckets per query descriptor and uses a recent rank aggregation method, OMEDRANK, in order to provide good approximate results for the nearest neighbour problem.We have recently shown that PvS-indexing works well for large collections of real image data. In that work, however, we used a simple scoring scheme and collected few nearest neighbours for each query descriptor. In this study we examine how much the actual number of nearest neighbours, gathered for each local descriptor, influences the final query result, when searching a PvS-index. Based on the results we propose two new alternative scoring schemes, which improve the retrieval quality and stabilise the results, making the search less affected by the actual number of nearest neighbours accumulated.