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
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Proceedings of the international conference on Multimedia information retrieval
MI-SIFT: mirror and inversion invariant generalization for SIFT descriptor
Proceedings of the ACM International Conference on Image and Video Retrieval
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
On image retrieval using salient regions with vector-spaces and latent semantics
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Open source column: OpenIMAJ - intelligent multimedia analysis in Java
ACM SIGMultimedia Records
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The SIFT keypoint descriptor is a powerful approach to encoding local image description using edge orientation histograms. Through codebook construction via k-means clustering and quantisation of SIFT features we can achieve image retrieval treating images as bags-of-words. Intensity inversion of images results in distinct SIFT features for a single local image patch across the two images. Intensity inversions notwithstanding these two patches are structurally identical. Through careful reordering of the SIFT feature vectors, we can construct the SIFT feature that would have been generated from a non-inverted image patch starting with those extracted from an inverted image patch. Furthermore, through examination of the local feature detection stage, we can estimate whether a given SIFT feature belongs in the space of inverted features, or non-inverted features. Therefore we can consistently separate the space of SIFT features into two distinct subspaces. With this knowledge, we can demonstrate reduced time complexity of codebook construction via clustering by up to a factor of four and also reduce the memory consumption of the clustering algorithms while producing equivalent retrieval results.