@conference {13588,
	title = {Logo Matching for Document Image Retrieval},
	booktitle = {International Conference on Document Analysis and Recognition (ICDAR 2009)},
	year = {2009},
	month = {2009///},
	pages = {606 - 610},
	abstract = {Graphics detection and recognition are fundamental research problems in document image analysis and retrieval. As one of the most pervasive graphical elements in business and government documents, logos may enable immediate identification of organizational entities and serve extensively as a declaration of a document{\textquoteright}s source and ownership. In this work, we developed an automatic logo-based document image retrieval system that handles: 1) Logo detection and segmentation by boosting a cascade of classifiers across multiple image scales; and 2) Logo matching using translation, scale, and rotation invariant shape descriptors and matching algorithms. Our approach is segmentation free and layout independent and we address logo retrieval in an unconstrained setting of 2-D feature point matching. Finally, we quantitatively evaluate the effectiveness of our approach using large collections of real-world complex document images.},
	author = {Zhu,Guangyu and David Doermann}
}
