Title :
Enhancing DPF for near-replica image recognition
Author :
Meng, Yan ; Chang, Edward ; Li, Beitao
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of California, Santa Barbara, CA, USA
Abstract :
Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown to be very effective in near-replica image recognition. DPF, however, suffers from the one-size-fits-all problem: it requires that all pairwise similarity measurements must use the same number of features. We propose methods for enhancing DPF´s performance by allowing different numbers of features to be selected in a pairwise manner. Through extensive empirical studies, we show that our three schemes: thresholding, sampling and weighting, and hybrid schemes of these three basic approaches, substantially outperform DPF in near-replica image recognition.
Keywords :
feature extraction; image coding; image matching; image recognition; image sampling; DPF enhancement; DPF performance; Internet; World Wide Web; copyrighted image; dynamic feature selection; dynamic partial function; near-replica image recognition; one-size-fits-all problem; pairwise image similarity measurement; sampling; thresholding; weighting; Computer Society; Computer vision; Image recognition; Pattern recognition;
Conference_Titel :
Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
Print_ISBN :
0-7695-1900-8
DOI :
10.1109/CVPR.2003.1211498