An algorithm for approximate closest-point queries
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Multidimensional binary search trees used for associative searching
Communications of the ACM
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Space-Time Completion of Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Principal Component Analysis to Multikey Searching
IEEE Transactions on Software Engineering
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A fast nearest neighbor search algorithm by nonlinear embedding
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Computing the dense Approximate Nearest-Neighbour Field (ANNF) between a pair of images has become a major problem which is being tackled by the image processing community in the recent years. Two important papers viz. PatchMatch [3] and CSH [11] have been developed over the past few years based on the coherency between images, but one major problem both these papers have is that image patches are treated as high dimensional vector features. In this paper we present a novel idea to reduce the dimensions of a p-by-p patch of color image to a set of low level features. This reduced dimension feature vector is used to compute the ANNF. Using these features we show that instead of dealing with image patches as p2 dimensional vectors, dealing with them in a lower dimension gives a much better approximation for the nearest-neighbour field as compared to the state of the art. We further present a modification which improves the ANNF to give more accurate color information and show that using our improved algorithm we do not need a pair of related images to compute the ANNF like in other algorithms, i.e. we can generate the ANNF for all the images using unrelated image pairs or even from a universal source image.