Multidimensional access methods
ACM Computing Surveys (CSUR)
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of DFT and DWT based similarity search in time-series databases
Proceedings of the ninth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Similarity Search in Multimedia Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Convex Optimization
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Orthogonal locality preserving indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning image manifolds by semantic subspace projection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
The VLDB Journal — The International Journal on Very Large Data Bases
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Automated image annotation using global features and robust nonparametric density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Effective data co-reduction for multimedia similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Spectral Regression dimension reduction for multiple features facial image retrieval
International Journal of Biometrics
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
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Content-based image similarity search plays a key role in multimedia retrieval. Each image is usually represented as a point in a high-dimensional feature space. The key challenge of searching similar images from a large database is the high computational overhead due to the "curse of dimensionality". Reducing the dimensionality is an important means to tackle the problem. In this paper, we study dimensionality reduction for top-k image retrieval. Intuitively, an effective dimensionality reduction method should not only preserve the close locations of similar images (or points), but also separate those dissimilar ones far apart in the reduced subspace. Existing dimensionality reduction methods mainly focused on the former. We propose a novel idea called Locality Condensation (LC) to not only preserve localities determined by neighborhood information and their global similarity relationship, but also ensure that different localities will not invade each other in the low-dimensional subspace. To generate non-overlapping localities in the subspace, LC first performs an elliptical condensation, which condenses each locality with an elliptical shape into a more compact hypersphere to enlarge the margins among different localities and estimate the projection in the subspace for overlap analysis. Through a convex optimization, LC further performs a scaling condensation on the obtained hyperspheres based on their projections in the subspace with minimal condensation degrees. By condensing the localities effectively, the potential overlaps among different localities in the low-dimensional subspace are prevented. Consequently, for similarity search in the subspace, the number of false hits (i.e., distant points that are falsely retrieved) will be reduced. Extensive experimental comparisons with existing methods demonstrate the superiority of our proposal.