Pattern Recognition Letters
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
One-Class Classification for Spontaneous Facial Expression Analysis
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Image tag re-ranking by coupled probability transition
Proceedings of the 20th ACM international conference on Multimedia
Query expansion enhancement by fast binary matching
Proceedings of the 20th ACM international conference on Multimedia
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Can we take advantage of the huge number of online images to improve image search quality? Motivated by this question, we propose a novel model to re-rank Google image search results by exploring the latent characteristic of massive unrelated images as a clue to filter them in the reranking. Inspired by the characteristic of the intrinsic diversity and the unwanted availability of the unrelated images, in our model, we adopt one-class classification to build a hyper-sphere for the target objects, unrelated images, and construct a robust boundary to distinguish them from the related images effectively. Then the Google results can be easily re-ranked by filtering the unrelated images with the built-up model. Extensive experiments demonstrate our approach outperforms Google image search engine's results, even if its baseline is high.