Modern Information Retrieval
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object-based tag propagation for semi-automatic annotation of images
Proceedings of the international conference on Multimedia information retrieval
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Smooth object retrieval using a bag of boundaries
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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We describe a retrieval based method for automatically determining the title and sculptor of an imaged sculpture. This is a useful problem to solve, but also quite challenging given the variety in both form and material that sculptures can take, and the similarity in both appearance and names that can occur. Our approach is to first visually match the sculpture and then to name it by harnessing the meta-data provided by Flickr users. To this end we make the following three contributions: (i) we show that using two complementary visual retrieval methods (one based on visual words, the other on boundaries) improves both retrieval and precision performance; (ii) we show that a simple voting scheme on the tf-idf weighted meta-data can correctly hypothesize a subset of the sculpture name (provided that the meta-data has first been suitably cleaned up and normalized); and (iii) we show that Google image search can be used to query expand the name sub-set, and thereby correctly determine the full name of the sculpture. The method is demonstrated on over 500 sculptors covering more than 2000 sculptures. We also quantitatively evaluate the system and demonstrate correct identification of the sculpture on over 60% of the queries.