A generalized multiple projection axes method for fast encoding of vector quantization
Pattern Recognition Letters
Reversible steganographic method using SMVQ approach based on declustering
Information Sciences: an International Journal
A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
A Unified Hierarchical Appearance Model for People Re-identification Using Multi-view Vision Sensors
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A color image hiding scheme based on SMVQ and modulo operator
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
VQ image steganographic method with high embedding capacity using multi-way search approach
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A Novel Index Coding Scheme for Vector Quantization
Fundamenta Informaticae
Reversible Data Embedding Based on Prediction Approach for VQ and SMVQ Compressed Images
Fundamenta Informaticae
Feature match: an efficient low dimensional PatchMatch technique
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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In this paper, we shall introduce a concept widely used by statisticians, the principal component analysis technique. We shall show that this principal component analysis technique can be used to create new keys from a set of old keys. These new keys are very useful in narrowing down the search domain. We shall also show that the projections on the first principal component direction can be viewed as hashing addresses for the best-match searching problem.