Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Learning image manifolds by semantic subspace projection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Regularized regression on image manifold for retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Locality sensitive semi-supervised feature selection
Neurocomputing
Relevance Feedback Learning for Web Image Retrieval Using Soft Support Vector Machine
Advanced Web and NetworkTechnologies, and Applications
A New Solution Scheme of Unsupervised Locality Preserving Projection Method for the SSS Problem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Image retrieval using nonlinear manifold embedding
Neurocomputing
Using large-scale web data to facilitate textual query based retrieval of consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Semantic discriminative projections for image retrieval
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
LPP solution schemes for use with face recognition
Pattern Recognition
Subspace similarity search: efficient k-NN queries in arbitrary subspaces
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Semi-supervised classification by local coordination
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Neighborhood preserving regression for image retrieval
Neurocomputing
Local block representation for face recognition
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Manifold coarse graining for online semi-supervised learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Exploiting the entire feature space with sparsity for automatic image annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A new proposal for locality preserving projection
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In relevance feedback driven image retrieval system, the user provided information can be used to better describe the intrinsic semantic relationships between images. In this paper, we propose a semi-supervised subspace learning algorithm which incrementally learns an adaptive subspace by preserving the semantic structure of the image space, based on user interactions in a relevance feedback driven query-by-example system. Our algorithm is capable of accumulating knowledge from users, which could result in new feature representations for images in the database so that the system's future retrieval performance can be enhanced. Experiments on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.