Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
LDAHash: Improved Matching with Smaller Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
BRIEF: Computing a Local Binary Descriptor Very Fast
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Supervised hashing with kernels
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
BRISK: Binary Robust invariant scalable keypoints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Binary SIFT: towards efficient feature matching verification for image search
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Scalar quantization for large scale image search
Proceedings of the 20th ACM international conference on Multimedia
Semi-Supervised Hashing for Large-Scale Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary Code Ranking with Weighted Hamming Distance
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper, we define a new metric, the Fragile Bit Weight (FBW), which is used in binary feature matching and measures how two features differ. High FBWs are associated with genuine matches between two binary features and low FBWs are associated with impostor ones. One bit in binary feature is deemed fragile if its sign of value reverses easily across the local image patch that has changed slightly. Previous binary feature extract algorithms ignore the fact that the signs of fragile bits are not stable through image transform. Rather than ignore fragile bits completely, we consider what beneficial information can be obtained from those fragile bits. In our approach, we exploit FBW as a measure in binary feature match to remove the false matches. In experiments, using FBW can effectively remove the false matches and highly improve the accuracy of feature match. Then, we find that fusion of FBW and Hamming distance work better in feature matching than Hamming distance alone. Furthermore, FBW can easily integrate in the well-established binary feature schemes if those descriptor bit in extract from comparison of pixels.