Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Combining Appearance and Topology for Wide Baseline Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data mining for image/video processing: a promising research frontier
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
A low-dimensional local descriptor incorporating TPS warping for image matching
Image and Vision Computing
Detection of near-duplicate patches in random images using keypoint-based features
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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In large visual databases, detection of prospectively similar contents requires simple and robust methods. Keypoint correspondences are a popular approach which, nevertheless, cannot detect (using typical descriptions) similarities in a wider image context, e.g. detection of similar fragments. For such capabilities, the analysis of configuration constraints is needed. We propose keypoint descriptions which (by using sets of words from large vocabularies) represent semi-local characteristics of images. Thus, similar image patches (including similarly looking objects) can be preliminarily retrieved by straightforward keypoint matching. A limited-scale experimental verification is provided. The approach can be prospectively used as a simple mid-level feature matching in large and unpredictable visual databases.