Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
Proposing a new architecture for mobile information and consultation support systems
TELE-INFO'05 Proceedings of the 4th WSEAS International Conference on Telecommunications and Informatics
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In order to find the best match to a query image in a database, conventional content-based image retrieval schemes need the exhaustive search, where the descriptor of the query, e.g., histogram, must be compared with literally all images in the database. However, the straightforward exhaustive search algorithm is computationally expensive. So, fast exhaustive search algorithms are demanded. This paper presents a fast exhaustive search algorithm based on a multi-resolution descriptor structure and a norm-sorted database. First, we derive a condition to eliminate unnecessary matching operations from the search procedure by using a norm-sorted structure of the database. Then, we propose a fast search algorithm based on the elimination condition, which guarantees an exhaustive search for either the best match or multiple best matches to a query. With a luminance histogram as a descriptor, we show that the proposed algorithm provides a search accuracy of 100% with high search speed.