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
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)
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
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Currently, Nearest-Neighbor approaches (NN) have been widely applied to real world image data mining. These approaches have the following three disadvantages: (i) the performance is inferior on small datasets; (ii) the performance of approximated nearest neighbor search will degrade for data with high dimensions; (iii) they are heavily dependent on the chosen feature and distance measure. To overcome these intrinsic weaknesses, we propose a novel Nearest-Neighbor method, which improves the original NN approaches from three aspects. Firstly, we propose a novel neighborhood similarity measure, where the similarity between test images and labeled images in the database is calculated jointly by the original image-to-image similarity and the average similarity of their neighboring unlabeled data. Secondly, we adopt the kernelized locality sensitive hashing to effectively conduct the nearest neighbor search for high dimensional data. Finally, to enhance the robustness of the method on different genres of images, we propose to fuse the discrimination power of different features by considering all the retrieved nearest neighbors via hashing systems using different features/kernels. Experimental result shows the advantage over traditional Nearest-Neighbor methods using the labeled data only. Even when the ratio of labeled data is very small, our method could also achieve remarkable results, thanks to the help of unlabeled data and multiple features.