article · 2008
Efficient Object Recognition and Image Retrieval
John Jaesung Lee, Trevor J. Darrell, John Jaesung Lee
Cited by 0Abstract and details only
Abstract
Algorithms for recognition and retrieval tasks generally call for both speed and accu-racy. When scaling up to very large applications, however, we encounter additional significant requirements: adaptability and scalability. In many real-world systems, large numbers of images are constantly added to the database, requiring the algo-rithm to quickly tune itself to recent trends so it can serve queries more effectively. Moreover, the systems need to be able to meet the demands of simultaneous queries from many users. In this thesis, I describe two new algorithms intended to meet these requirements and give an extensive experimental evaluation for both. The first algorithm constructs an adaptive vocabulary forest, which is an efficient image-database model that grows and shrinks as needed while adapting its structure to tune itself to recent trends. The second algorithm is a method for efficiently performing classification tasks by comparing query images to only a fixed number of training examples, regardless of
Cite this paper
Lee, J. J., Darrell, T. J., & Lee, J. J. (2008). Efficient Object Recognition and Image Retrieval.
Lee, John Jaesung, et al. “Efficient Object Recognition and Image Retrieval.” 2008.
Lee, John Jaesung, Trevor J. Darrell, and John Jaesung Lee. 2008. “Efficient Object Recognition and Image Retrieval.”
Lee, J.J., Darrell, T.J. and Lee, J.J. (2008) ‘Efficient Object Recognition and Image Retrieval’.
@article{lee2008efficient,
title = {{Efficient Object Recognition and Image Retrieval}},
author = {Lee, John Jaesung and Darrell, Trevor J. and Lee, John Jaesung},
year = {2008}
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