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STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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ACM SIGGRAPH 2007 papers
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
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Computer Vision and Image Understanding
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Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations and Trends® in Computer Graphics and Vision
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Proceedings of the 20th ACM international conference on Multimedia
Editorial: Modifications of the construction and voting mechanisms of the Random Forests Algorithm
Data & Knowledge Engineering
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In this paper, we present a novel method for image annotation and made three contributions. Firstly, we propose to use the tags contained in the training images as the supervising information to guide the generation of random trees, thus enabling the retrieved nearest neighbor images not only visually alike but also semantically related. Secondly, different from conventional decision tree methods, which fuse the information contained at each leaf node individually, our method treats the random forest as a whole, and introduces the new concepts of semantic nearest neighbors (SNN) and semantic similarity measure (SSM). Thirdly, we annotate an image from the tags of its SNN based on SSM and have developed a novel learning to rank algorithm to systematically assign the optimal tags to the image. The new technique is intrinsically scalable and we will present experimental results to demonstrate that it is competitive to state of the art methods.