Matrix computations (3rd ed.)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Spectral structuring of home videos
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Image clustering with tensor representation
Proceedings of the 13th annual ACM international conference on Multimedia
Graph based multi-modality learning
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
Maximum unfolded embedding: formulation, solution, and application for image clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Appearance-based video clustering in 2D locality preserving projection subspace
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
Computer Vision and Image Understanding
Leveraging user query log: toward improving image data clustering
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Classification of multivariate time series using locality preserving projections
Knowledge-Based Systems
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multi-modality video shot clustering with tensor representation
Multimedia Tools and Applications
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
Gabor feature based face recognition using supervised locality preserving projection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Multiple similarities based kernel subspace learning for image classification
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Incremental visual objects clustering with the growing vocabulary tree
Multimedia Tools and Applications
A new proposal for locality preserving projection
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Semantic image clustering using object relation network
CVM'12 Proceedings of the First international conference on Computational Visual Media
Constrained spectral embedding for K-way data clustering
Pattern Recognition Letters
A bag-of-semantics model for image clustering
The Visual Computer: International Journal of Computer Graphics
Hi-index | 0.00 |
It is important and challenging to make the growing image repositories easy to search and browse. Image clustering is a technique that helps in several ways, including image data preprocessing, user interface designing, and search result representation. Spectral clustering method has been one of the most promising clustering methods in the last few years, because it can cluster data with complex structure, and the (near) global optimum is guaranteed. However, existing spectral clustering algorithms, like Normalized Cut, are difficult to handle data points out of training set. In this paper, we propose a clustering algorithm named Locality Preserving Clustering (LPC), which shares many of the data representation properties of nonlinear spectral method. Yet LPC provides an explicit mapping function which is defined everywhere, both on training data points and testing points. Experimental results show that LPC is more accurate than both "direct Kmeans" and "PCA + Kmeans". We also show that LPC produces in general comparable results with Normalized Cut, yet is more efficient than Normalized Cut.