Fast Vector Matching Methods and Their Applications to Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Accelerating feature-vector matching using multiple-tree and sub-vector methods
Pattern Recognition
Efficient multi-resolution histogram matching for fast image/video retrieval
Pattern Recognition Letters
Local feature analysis for robust face recognition
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Efficient human action and gait analysis using multiresolution motion energy histogram
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
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Most of the content-based image retrieval systems require a distance computation for each candidate image in the database. As a brute-force approach, the exhaustive search can be employed for this computation. However, this exhaustive search is time-consuming and limits the usefulness of such systems. Thus, there is a growing demand for a fast algorithm which provides the same retrieval results as the exhaustive search. We propose a fast search algorithm based on a multiresolution data structure. The proposed algorithm computes the lower bound of distance at each level and compares it with the latest minimum distance, starting from the low-resolution level. Once it is larger than the latest minimum distance, we can remove the candidates without calculating the full-resolution distance. By doing this, we can dramatically reduce the total computational complexity. It is noticeable that the proposed fast algorithm provides not only the same retrieval results as the exhaustive search, but also a faster searching ability than existing fast algorithms. For additional performance improvement, we can easily combine the proposed algorithm with existing tree-based algorithms. The algorithm can also be used for the fast matching of various features such as luminance histograms, edge histograms, and local binary partition textures