In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A Survey on Mobile Landmark Recognition for Information Retrieval
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Distances and weighting schemes for bag of visual words image retrieval
Proceedings of the international conference on Multimedia information retrieval
MMEDIA '10 Proceedings of the 2010 Second International Conferences on Advances in Multimedia
kNN based image classification relying on local feature similarity
Proceedings of the Third International Conference on SImilarity Search and APplications
Sub-image searching through intersection of local descriptors
Proceedings of the Third International Conference on SImilarity Search and APplications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Proximity-Based order-respecting intersection for searching in image databases
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
| Hi-index | 0.00 |
Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kNN classifications based on the use of local image features, such as SIFT or SURF. Quality of image classification can be improved by defining geometric consistency check rules based on space transformations of the scene depicted in images. However, this prevents the use of state of the art access methods for similarity searching and sequential scan of the images in the training sets has to be executed in order to perform classification. In this paper we propose a technique that allows one to use access methods for similarity searching, such as those exploiting metric space properties, in order to perform kNN classification with geometric consistency checks. We will see that the proposed approach, in addition to offer an obvious efficiency improvement, surprisingly offers also an improvement of the effectiveness of the classification.