Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Statistical modelling of outliers for fast visual search
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Contextual Bag-of-Words for Visual Categorization
IEEE Transactions on Circuits and Systems for Video Technology
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Visual query-by-capture applications call for a compact visual descriptor with minimum descriptor length. Preserving the visual identification performance while minimising the bit rate is a focus of the on-going MPEG7 CDVS (Compact Descriptors for Visual Search) standardisation effort. In this paper we tackle this problem by adopting Laplacian embedding for SIFT feature compression and employing topology verification based on a novel graph cut measure. In contrast to previous feature compression schemes, we approach the problem by finding a Laplacian embedding that preserves the nearest neighbour relations in feature space. Furthermore, we develop an efficient yet effective topology verification (TV) scheme to perform spatial consistency checking. In contrast to previous works on geometric verification, instead of enumerating all possible combinations of coordinate alignments of an image pair, this TV solution verifies possibly misaligned coordinate sets with a learning method which acquires a proper boundary between the topology representation of matched and non-matched image pairs. Furthermore, this TV solution is invariant to in-plane rotation, scaling and is quite resilient to a range of out-of-plane rotations. The proposed Laplacian embedding and Topological verification scheme are tested with the CDVS dataset and are found to be effective.