Algorithms for clustering data
Algorithms for clustering data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
AutoAlbum: Clustering Digital Photographs using Probabilistic Model Merging
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Temporal event clustering for digital photo collections
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
IGroup: presenting web image search results in semantic clusters
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Image clustering based on a shared nearest neighbors approach for tagged collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Distance metric learning vs. Fisher discriminant analysis
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Smart batch tagging of photo albums
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Personal photo album summarization
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
ClustTour: city exploration by use of hybrid photo clustering
Proceedings of the international conference on Multimedia
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Spectral K-way ratio-cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Unsupervised image-set clustering using an information theoretic framework
IEEE Transactions on Image Processing
Information Sciences: an International Journal
Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot
Information Sciences: an International Journal
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Photo clustering is an effective way to organize albums and it is useful in many applications, such as photo browsing and tagging. But automatic photo clustering is not an easy task due to the large variation of photo content. In this paper, we propose an interactive photo clustering paradigm that jointly explores human and computer. In this paradigm, the photo clustering task is semi-automatically accomplished: users are allowed to manually adjust clustering results with different operations, such as splitting clusters, merging clusters and moving photos from one cluster to another. Behind users' operations, we have a learning engine that keeps updating the distance measurements between photos in an online way, such that better clustering can be performed based on the distance measure. Experimental results on multiple photo albums demonstrated that our approach is able to improve automatic photo clustering results, and by exploring distance metric learning, our method is much more effective than pure manual adjustments of photo clustering.