@inproceedings{54e37fb8d2b64a018872bdb5122f9294,
title = "SIGMA: Spatial integrated matching association algorithm for logo detection",
abstract = "In this paper, we adopt the integration model of spatial feature correlations to order the indexing and matching features, and address the computational ineffectiveness and inefficiency of local features based logo detection methods. We propose a Spatial InteGrated Matching Association algorithm (SIGMA) for logo detection in natural scene that contains extremely variances in viewpoints, illuminations and occlusions. Our SIGMA algorithm consists of two phases: the Spatial InteGrated (SIG) phase and the Matching Association (MA) phase. The SIG phase integrates spatial correlations in feature representation, while the MA phase improves the matching performance by ordering an optimized matching sequence. We have collected a logo dataset containing 2,400 photos with 12 logo categories from Flickr, and experimental results demonstrate that the performance of proposed approach outperforms the state-of-the-art approaches on the dataset.",
keywords = "Feature set matching, Local feature presentation, Logo detection, SIGMA, Spatial correlation tree",
author = "Pengfei Xu and Hongxun Yao and Rongrong Ji",
year = "2010",
doi = "10.1109/ICASSP.2010.5495345",
language = "英语",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1086--1089",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
address = "美国",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}