Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Thick boundaries in binary space and their influence on nearest-neighbor search
Pattern Recognition Letters
Viewpoint-aware object detection and continuous pose estimation
Image and Vision Computing
Efficient discriminative projections for compact binary descriptors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Comparative evaluation of binary features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A convolutional treelets binary feature approach to fast keypoint recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Densifying Distance Spaces for Shape and Image Retrieval
Journal of Mathematical Imaging and Vision
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Effective hashing for large-scale multimedia search
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Topology preserving hashing for similarity search
Proceedings of the 21st ACM international conference on Multimedia
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Linear cross-modal hashing for efficient multimedia search
Proceedings of the 21st ACM international conference on Multimedia
Improved binary feature matching through fusion of hamming distance and fragile bit weight
Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices
Large-scale Structure-from-Motion Reconstruction with small memory consumption
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Smart hashing update for fast response
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128--dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach.