The analysis of a simple k-means clustering algorithm
Proceedings of the sixteenth annual symposium on Computational geometry
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Automatic Document Logo Detection
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Logo Spotting by a Bag-of-words Approach for Document Categorization
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Fast Logo Detection and Recognition in Document Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Document Logo Detection and Recognition Using Bayesian Model
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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In this paper, we present a methodology to categorize camera captured documents into pre-defined logo classes. Unlike scanned documents, camera captured documents suffer from intensity variations, partial occlusions, cluttering, and large scale variations. Furthermore, the existence of non-uniform folds and the lack of document being flat make this task more challenging. We present the selection of robust local features and the corresponding parameters by comparisons among SIFT, SURF, MSER, Hessian-affine, and Harris-affine. We evaluate the system not only with respect to amount of space required to store the local features information but also with respect to categorization accuracy. Moreover, the system handles the identification of multiple logos on the document at the same time. Experimental results on a challenging set of real images demonstrate the efficiency of our approach.