Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multidimensional binary search trees used for associative searching
Communications of the ACM
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
Semi-supervised protein classification using cluster kernels
Bioinformatics
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Distance metric learning from uncertain side information with application to automated photo tagging
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
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
S3MKL: scalable semi-supervised multiple kernel learning for image data mining
Proceedings of the international conference on Multimedia
Nearest-neighbor classification using unlabeled data for real world image application
Proceedings of the international conference on Multimedia
Multiple Kernel Learning with High Order Kernels
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Video accessibility enhancement for hearing-impaired users
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Hi-index | 0.01 |
Currently, Nearest-Neighbor approaches (NN) have been applied to large scale real world image data mining. However, the following three disadvantages prevent them from wider application compared to other machine learning methods: (i) the performance is inferior on small datasets; (ii) the performance will degrade for data with high dimensions; (iii) they are heavily dependent on the chosen feature and distance measure. In this paper, we try to overcome the three mentioned intrinsic weaknesses by taking the abundant and diversified content of social media images into account. Firstly, we propose a novel neighborhood similarity measure which encodes both the local density information and semantic information, thus it has better generalization power than the original image-to-image similarity. Secondly, to enhance the scalability, we adopt kernelized Locality Sensitive Hashing (KLSH) to conduct approximated nearest neighbor search by utilizing a set of kernels calculated on several complementary image features. Finally, to enhance the robustness on diversified genres of images, we propose to fuse the discrimination power of different features by combining multiple neighborhood similarities calculated on different features/kernels with the entire retrieved nearest labeled and unlabeled image via the hashing systems. Experimental results on visual categorization on the Caltech-256 and two social media databases show the advantage of our method over traditional NN methods using the labeled data only.