An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An information theoretic approach to content based image retrieval
An information theoretic approach to content based image retrieval
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Analysis and extension of spectral methods for nonlinear dimensionality reduction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
The Evaluation of Wavelet and Data Driven Feature Selection for Image Understanding
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
A unified image retrieval framework on local visual and semantic concept-based feature spaces
Journal of Visual Communication and Image Representation
Recover the tampered image based on VQ indexing
Signal Processing
Signature Detection and Matching for Document Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval using visually significant point features
Fuzzy Sets and Systems
Content based image retrieval using unclean positive examples
IEEE Transactions on Image Processing
Research of Image Retrieval Based on Color
IFCSTA '09 Proceedings of the 2009 International Forum on Computer Science-Technology and Applications - Volume 01
Spline embedding for nonlinear dimensionality reduction
ECML'06 Proceedings of the 17th European conference on Machine Learning
Image Retrieval based on HSV Feature and Regional Shannon Entropy
International Journal of Software Science and Computational Intelligence
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In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm APGA by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.